------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\sgolder\Dropbox\OUP_Multilevel_Book\OUP multilevel electoral behavior book\chapt
> er 7\replication\ch7_analyses.log
  log type:  text
 opened on:  22 Jun 2017, 21:59:25

. set more off

. 
. use "C:\Users\sgolder\Dropbox\OUP_Multilevel_Book\OUP multilevel electoral behavior book\chapter 7\r
> eplication\ch7.dta", clear

. 
. 
. 
. ********************************************************
. 
. **** EXPLAINING EVALUATIONS Table 7.1
. 
. **** National Economic Evaluations at time of national survey
. 
. 
. *** IDF national
. logit worsenational i.female age schooling  i.partyID i.opposeID  if ELECID==5 

Iteration 0:   log likelihood = -559.59014  
Iteration 1:   log likelihood = -515.93183  
Iteration 2:   log likelihood = -514.55334  
Iteration 3:   log likelihood = -514.54969  
Iteration 4:   log likelihood = -514.54969  

Logistic regression                             Number of obs     =        966
                                                LR chi2(5)        =      90.08
                                                Prob > chi2       =     0.0000
Log likelihood = -514.54969                     Pseudo R2         =     0.0805

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.0952528   .1548876    -0.61   0.539    -.3988268    .2083213
          age |    .023753   .0055688     4.27   0.000     .0128384    .0346677
    schooling |   .1940029   .0462563     4.19   0.000     .1033423    .2846635
    1.partyID |   -1.01339   .2051094    -4.94   0.000    -1.415397   -.6113825
   1.opposeID |   .8424649   .1842397     4.57   0.000     .4813617    1.203568
        _cons |  -1.027797   .3876283    -2.65   0.008    -1.787534   -.2680593
-------------------------------------------------------------------------------

. 
. *** Provence national
. logit worsenational  i.female age schooling i.partyID i.opposeID  if ELECID==6 

Iteration 0:   log likelihood =  -564.7949  
Iteration 1:   log likelihood = -516.62941  
Iteration 2:   log likelihood = -514.98579  
Iteration 3:   log likelihood = -514.97978  
Iteration 4:   log likelihood = -514.97978  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =      99.63
                                                Prob > chi2       =     0.0000
Log likelihood = -514.97978                     Pseudo R2         =     0.0882

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |    .180731    .154007     1.17   0.241    -.1211172    .4825792
          age |   .0349529   .0054542     6.41   0.000     .0242628     .045643
    schooling |   .1936411   .0454429     4.26   0.000     .1045746    .2827076
    1.partyID |  -.7773089   .2035357    -3.82   0.000    -1.176232   -.3783863
   1.opposeID |   .7593975   .1808633     4.20   0.000     .4049119    1.113883
        _cons |  -1.770455   .3946459    -4.49   0.000    -2.543946   -.9969631
-------------------------------------------------------------------------------

. 
. *** Catalonia national
. logit worsenational  i.female age schooling  i.partyID i.opposeID  if ELECID==7

Iteration 0:   log likelihood = -434.07064  
Iteration 1:   log likelihood =  -420.8049  
Iteration 2:   log likelihood = -420.43786  
Iteration 3:   log likelihood = -420.43723  
Iteration 4:   log likelihood = -420.43723  

Logistic regression                             Number of obs     =        951
                                                LR chi2(5)        =      27.27
                                                Prob > chi2       =     0.0001
Log likelihood = -420.43723                     Pseudo R2         =     0.0314

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.2381572   .1758474    -1.35   0.176    -.5828117    .1064974
          age |   .0276074   .0070713     3.90   0.000     .0137479    .0414668
    schooling |   .1513814   .0786802     1.92   0.054    -.0028289    .3055917
    1.partyID |  -.6213804   .2824346    -2.20   0.028    -1.174942   -.0678188
   1.opposeID |   .5091473   .3368635     1.51   0.131    -.1510929    1.169388
        _cons |   .2233815   .3866502     0.58   0.563    -.5344391     .981202
-------------------------------------------------------------------------------

. 
. *** Madrid national
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==9 

Iteration 0:   log likelihood = -408.37778  
Iteration 1:   log likelihood = -391.29301  
Iteration 2:   log likelihood = -388.90919  
Iteration 3:   log likelihood = -388.90357  
Iteration 4:   log likelihood = -388.90357  

Logistic regression                             Number of obs     =        976
                                                LR chi2(5)        =      38.95
                                                Prob > chi2       =     0.0000
Log likelihood = -388.90357                     Pseudo R2         =     0.0477

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.1029364   .1870778    -0.55   0.582    -.4696022    .2637295
          age |   .0100631   .0082448     1.22   0.222    -.0060965    .0262226
    schooling |   .1819833   .0812498     2.24   0.025     .0227366      .34123
    1.partyID |   -1.13154   .2456457    -4.61   0.000    -1.612997   -.6500837
   1.opposeID |   .6285236   .2798536     2.25   0.025     .0800207    1.177027
        _cons |    .932098   .4416717     2.11   0.035     .0664373    1.797759
-------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==10 

Iteration 0:   log likelihood = -618.50107  
Iteration 1:   log likelihood = -602.91029  
Iteration 2:   log likelihood = -602.65276  
Iteration 3:   log likelihood =  -602.6524  
Iteration 4:   log likelihood =  -602.6524  

Logistic regression                             Number of obs     =        975
                                                LR chi2(5)        =      31.70
                                                Prob > chi2       =     0.0000
Log likelihood =  -602.6524                     Pseudo R2         =     0.0256

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .2538721   .1399175     1.81   0.070    -.0203611    .5281054
          age |    .009363   .0050942     1.84   0.066    -.0006214    .0193475
    schooling |  -.0451973    .041883    -1.08   0.281    -.1272864    .0368918
    1.partyID |  -1.216913   .2975771    -4.09   0.000    -1.800153   -.6336723
   1.opposeID |   .1050777   .1546572     0.68   0.497    -.1980448    .4082002
        _cons |  -1.087066   .2998668    -3.63   0.000    -1.674794   -.4993375
-------------------------------------------------------------------------------

. 
. logit betternational  i.female age schooling i.partyID i.opposeID   if ELECID==10 

Iteration 0:   log likelihood = -533.81951  
Iteration 1:   log likelihood = -497.65974  
Iteration 2:   log likelihood = -495.49276  
Iteration 3:   log likelihood = -495.48492  
Iteration 4:   log likelihood = -495.48492  

Logistic regression                             Number of obs     =        975
                                                LR chi2(5)        =      76.67
                                                Prob > chi2       =     0.0000
Log likelihood = -495.48492                     Pseudo R2         =     0.0718

--------------------------------------------------------------------------------
betternational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -.4347583   .1595649    -2.72   0.006    -.7474998   -.1220168
           age |  -.0065848   .0057447    -1.15   0.252    -.0178442    .0046746
     schooling |  -.0026122   .0467934    -0.06   0.955    -.0943257    .0891012
     1.partyID |     1.8501    .233979     7.91   0.000      1.39151     2.30869
    1.opposeID |   .0715238    .185775     0.39   0.700    -.2925885     .435636
         _cons |  -.9208491   .3306878    -2.78   0.005    -1.568985   -.2727128
--------------------------------------------------------------------------------

. 
. *** Bavaria national
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==12

Iteration 0:   log likelihood = -2431.8622  
Iteration 1:   log likelihood = -2295.1903  
Iteration 2:   log likelihood = -2287.5063  
Iteration 3:   log likelihood = -2287.4739  
Iteration 4:   log likelihood = -2287.4739  

Logistic regression                             Number of obs     =      4,691
                                                LR chi2(5)        =     288.78
                                                Prob > chi2       =     0.0000
Log likelihood = -2287.4739                     Pseudo R2         =     0.0594

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .2988428   .0747517     4.00   0.000     .1523322    .4453534
          age |   .0089437   .0028803     3.11   0.002     .0032985    .0145889
    schooling |  -.1673283    .020529    -8.15   0.000    -.2075644   -.1270923
    1.partyID |  -1.464927   .1377071   -10.64   0.000    -1.734828   -1.195026
   1.opposeID |  -.0872907   .0837105    -1.04   0.297    -.2513603     .076779
        _cons |  -1.125313   .1639968    -6.86   0.000    -1.446741   -.8038856
-------------------------------------------------------------------------------

. 
. logit betternational  i.female age schooling i.partyID i.opposeID   if ELECID==12

Iteration 0:   log likelihood = -2941.2644  
Iteration 1:   log likelihood = -2728.9167  
Iteration 2:   log likelihood = -2726.5502  
Iteration 3:   log likelihood = -2726.5497  
Iteration 4:   log likelihood = -2726.5497  

Logistic regression                             Number of obs     =      4,687
                                                LR chi2(5)        =     429.43
                                                Prob > chi2       =     0.0000
Log likelihood = -2726.5497                     Pseudo R2         =     0.0730

--------------------------------------------------------------------------------
betternational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -.4543415   .0669651    -6.78   0.000    -.5855906   -.3230923
           age |  -.0072529   .0025549    -2.84   0.005    -.0122604   -.0022455
     schooling |   .0947416   .0171337     5.53   0.000     .0611602    .1283229
     1.partyID |   1.388948   .0834895    16.64   0.000     1.225312    1.552585
    1.opposeID |   .3196555   .0789027     4.05   0.000      .165009    .4743021
         _cons |  -.9402721   .1458724    -6.45   0.000    -1.226177   -.6543675
--------------------------------------------------------------------------------

. 
. ***Subnational Economic Evaluations at time of subnational survey
. 
. *** Paris subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==22

Iteration 0:   log likelihood = -792.22803  
Iteration 1:   log likelihood = -764.92346  
Iteration 2:   log likelihood = -764.77434  
Iteration 3:   log likelihood = -764.77431  

Logistic regression                             Number of obs     =      1,208
                                                LR chi2(5)        =      54.91
                                                Prob > chi2       =     0.0000
Log likelihood = -764.77431                     Pseudo R2         =     0.0347

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.2808809   .1226359    -2.29   0.022    -.5212429   -.0405188
          age |   .0210077   .0041122     5.11   0.000      .012948    .0290674
    schooling |   .1599448   .0534545     2.99   0.003     .0551759    .2647138
    1.partyID |  -.4442812   .1640092    -2.71   0.007    -.7657334   -.1228291
   1.opposeID |   .3636586   .1471806     2.47   0.013     .0751898    .6521273
        _cons |  -2.193727   .3816576    -5.75   0.000    -2.941762   -1.445692
-------------------------------------------------------------------------------

. 
. *** Marseille subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==23

Iteration 0:   log likelihood = -500.59274  
Iteration 1:   log likelihood = -488.39602  
Iteration 2:   log likelihood = -488.36587  
Iteration 3:   log likelihood = -488.36587  

Logistic regression                             Number of obs     =        725
                                                LR chi2(5)        =      24.45
                                                Prob > chi2       =     0.0002
Log likelihood = -488.36587                     Pseudo R2         =     0.0244

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.1329154   .1541207    -0.86   0.388    -.4349864    .1691555
          age |    .012433   .0053859     2.31   0.021     .0018769    .0229891
    schooling |   -.107366   .0603932    -1.78   0.075    -.2257345    .0110024
    1.partyID |  -.8036419   .2427952    -3.31   0.001    -1.279512   -.3277721
   1.opposeID |   .0814543   .1713948     0.48   0.635    -.2544733     .417382
        _cons |  -.0740126   .4282677    -0.17   0.863    -.9134018    .7653766
-------------------------------------------------------------------------------

. 
. *** Catalonia subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==8

Iteration 0:   log likelihood = -425.00412  
Iteration 1:   log likelihood = -409.86729  
Iteration 2:   log likelihood = -409.17422  
Iteration 3:   log likelihood = -409.17311  
Iteration 4:   log likelihood = -409.17311  

Logistic regression                             Number of obs     =        993
                                                LR chi2(5)        =      31.66
                                                Prob > chi2       =     0.0000
Log likelihood = -409.17311                     Pseudo R2         =     0.0372

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .4193548   .1813194     2.31   0.021     .0639753    .7747343
          age |   .0183652   .0064423     2.85   0.004     .0057385    .0309919
    schooling |   .0634995    .078985     0.80   0.421    -.0913081    .2183072
    1.partyID |  -.7981373   .2405519    -3.32   0.001     -1.26961   -.3266643
   1.opposeID |   .4711212   .2279685     2.07   0.039     .0243111    .9179312
        _cons |   .5682915   .3533871     1.61   0.108    -.1243345    1.260917
-------------------------------------------------------------------------------

. 
. *** Madrid subnational no data
. 
. *** Lower Saxony subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==11

Iteration 0:   log likelihood =  -564.7949  
Iteration 1:   log likelihood = -546.02253  
Iteration 2:   log likelihood = -545.42696  
Iteration 3:   log likelihood =  -545.4244  
Iteration 4:   log likelihood =  -545.4244  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =      38.74
                                                Prob > chi2       =     0.0000
Log likelihood =  -545.4244                     Pseudo R2         =     0.0343

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |    .354248   .1520616     2.33   0.020     .0562127    .6522833
          age |   .0066205   .0053712     1.23   0.218    -.0039068    .0171478
    schooling |  -.1131146   .0457315    -2.47   0.013    -.2027466   -.0234825
    1.partyID |  -1.298863   .3193997    -4.07   0.000    -1.924875   -.6728506
   1.opposeID |  -.0834941   .1837245    -0.45   0.650    -.4435874    .2765993
        _cons |  -1.080435    .332285    -3.25   0.001    -1.731702   -.4291683
-------------------------------------------------------------------------------

.  
. logit  betterregional  i.female age schooling i.partyID i.opposeID if ELECID==11

Iteration 0:   log likelihood = -431.73089  
Iteration 1:   log likelihood = -389.28534  
Iteration 2:   log likelihood = -380.48127  
Iteration 3:   log likelihood = -380.44209  
Iteration 4:   log likelihood = -380.44209  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =     102.58
                                                Prob > chi2       =     0.0000
Log likelihood = -380.44209                     Pseudo R2         =     0.1188

--------------------------------------------------------------------------------
betterregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -1.038794   .1978914    -5.25   0.000    -1.426653   -.6509336
           age |   .0014583   .0064063     0.23   0.820    -.0110979    .0140145
     schooling |    .022863   .0545565     0.42   0.675    -.0840658    .1297919
     1.partyID |    1.76649   .2262581     7.81   0.000     1.323032    2.209948
    1.opposeID |  -.2481862   .2532359    -0.98   0.327    -.7445195    .2481471
         _cons |  -1.649553   .3892201    -4.24   0.000    -2.412411   -.8866958
--------------------------------------------------------------------------------

. 
. 
. *** Bavaria subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==13

Iteration 0:   log likelihood =  -2026.242  
Iteration 1:   log likelihood = -1926.3241  
Iteration 2:   log likelihood =  -1916.903  
Iteration 3:   log likelihood = -1916.8294  
Iteration 4:   log likelihood = -1916.8294  

Logistic regression                             Number of obs     =      5,906
                                                LR chi2(5)        =     218.83
                                                Prob > chi2       =     0.0000
Log likelihood = -1916.8294                     Pseudo R2         =     0.0540

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .4346712   .0884708     4.91   0.000     .2612716    .6080709
          age |   .0029176   .0033282     0.88   0.381    -.0036056    .0094408
    schooling |  -.1497546   .0241288    -6.21   0.000    -.1970462    -.102463
    1.partyID |  -1.464854   .1622692    -9.03   0.000    -1.782896   -1.146812
   1.opposeID |   .1132831   .1091807     1.04   0.299    -.1007071    .3272732
        _cons |  -1.832346   .1908318    -9.60   0.000    -2.206369   -1.458322
-------------------------------------------------------------------------------

. 
. logit betterregional  i.female age schooling i.partyID i.opposeID if ELECID==13

Iteration 0:   log likelihood = -3620.4001  
Iteration 1:   log likelihood = -3299.2934  
Iteration 2:   log likelihood = -3295.5932  
Iteration 3:   log likelihood = -3295.5917  
Iteration 4:   log likelihood = -3295.5917  

Logistic regression                             Number of obs     =      5,745
                                                LR chi2(5)        =     649.62
                                                Prob > chi2       =     0.0000
Log likelihood = -3295.5917                     Pseudo R2         =     0.0897

--------------------------------------------------------------------------------
betterregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |    -.66208   .0610442   -10.85   0.000    -.7817244   -.5424356
           age |  -.0004427   .0022951    -0.19   0.847    -.0049411    .0040556
     schooling |   .0696996   .0155876     4.47   0.000     .0391485    .1002507
     1.partyID |   1.298024   .0685709    18.93   0.000     1.163627     1.43242
    1.opposeID |  -.2718367   .0898211    -3.03   0.002    -.4478828   -.0957907
         _cons |  -.9507966   .1319523    -7.21   0.000    -1.209418   -.6921748
--------------------------------------------------------------------------------

. 
. 
. ********************************************************
. *** auxilliary analyses with marginal effects based on analyses in Table 7.1
. 
. *** IDF national
. logit worsenational i.female age schooling  i.partyID i.opposeID  if ELECID==5 

Iteration 0:   log likelihood = -559.59014  
Iteration 1:   log likelihood = -515.93183  
Iteration 2:   log likelihood = -514.55334  
Iteration 3:   log likelihood = -514.54969  
Iteration 4:   log likelihood = -514.54969  

Logistic regression                             Number of obs     =        966
                                                LR chi2(5)        =      90.08
                                                Prob > chi2       =     0.0000
Log likelihood = -514.54969                     Pseudo R2         =     0.0805

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.0952528   .1548876    -0.61   0.539    -.3988268    .2083213
          age |    .023753   .0055688     4.27   0.000     .0128384    .0346677
    schooling |   .1940029   .0462563     4.19   0.000     .1033423    .2846635
    1.partyID |   -1.01339   .2051094    -4.94   0.000    -1.415397   -.6113825
   1.opposeID |   .8424649   .1842397     4.57   0.000     .4813617    1.203568
        _cons |  -1.027797   .3876283    -2.65   0.008    -1.787534   -.2680593
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        966
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.2051826    .044649    -4.60   0.000     -.292693   -.1176722
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        966
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .1443324   .0293927     4.91   0.000     .0867238     .201941
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit worsenational  i.female age schooling i.partyID i.opposeID  if ELECID==6 

Iteration 0:   log likelihood =  -564.7949  
Iteration 1:   log likelihood = -516.62941  
Iteration 2:   log likelihood = -514.98579  
Iteration 3:   log likelihood = -514.97978  
Iteration 4:   log likelihood = -514.97978  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =      99.63
                                                Prob > chi2       =     0.0000
Log likelihood = -514.97978                     Pseudo R2         =     0.0882

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |    .180731    .154007     1.17   0.241    -.1211172    .4825792
          age |   .0349529   .0054542     6.41   0.000     .0242628     .045643
    schooling |   .1936411   .0454429     4.26   0.000     .1045746    .2827076
    1.partyID |  -.7773089   .2035357    -3.82   0.000    -1.176232   -.3783863
   1.opposeID |   .7593975   .1808633     4.20   0.000     .4049119    1.113883
        _cons |  -1.770455   .3946459    -4.49   0.000    -2.543946   -.9969631
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1492297    .041732    -3.58   0.000     -.231023   -.0674364
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .1284906   .0289402     4.44   0.000     .0717687    .1852124
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit worsenational  i.female age schooling  i.partyID i.opposeID  if ELECID==7

Iteration 0:   log likelihood = -434.07064  
Iteration 1:   log likelihood =  -420.8049  
Iteration 2:   log likelihood = -420.43786  
Iteration 3:   log likelihood = -420.43723  
Iteration 4:   log likelihood = -420.43723  

Logistic regression                             Number of obs     =        951
                                                LR chi2(5)        =      27.27
                                                Prob > chi2       =     0.0001
Log likelihood = -420.43723                     Pseudo R2         =     0.0314

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.2381572   .1758474    -1.35   0.176    -.5828117    .1064974
          age |   .0276074   .0070713     3.90   0.000     .0137479    .0414668
    schooling |   .1513814   .0786802     1.92   0.054    -.0028289    .3055917
    1.partyID |  -.6213804   .2824346    -2.20   0.028    -1.174942   -.0678188
   1.opposeID |   .5091473   .3368635     1.51   0.131    -.1510929    1.169388
        _cons |   .2233815   .3866502     0.58   0.563    -.5344391     .981202
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        951
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0991868   .0510606    -1.94   0.052    -.1992637      .00089
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        951
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |    .061251   .0349761     1.75   0.080    -.0073009    .1298029
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==9 

Iteration 0:   log likelihood = -408.37778  
Iteration 1:   log likelihood = -391.29301  
Iteration 2:   log likelihood = -388.90919  
Iteration 3:   log likelihood = -388.90357  
Iteration 4:   log likelihood = -388.90357  

Logistic regression                             Number of obs     =        976
                                                LR chi2(5)        =      38.95
                                                Prob > chi2       =     0.0000
Log likelihood = -388.90357                     Pseudo R2         =     0.0477

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.1029364   .1870778    -0.55   0.582    -.4696022    .2637295
          age |   .0100631   .0082448     1.22   0.222    -.0060965    .0262226
    schooling |   .1819833   .0812498     2.24   0.025     .0227366      .34123
    1.partyID |   -1.13154   .2456457    -4.61   0.000    -1.612997   -.6500837
   1.opposeID |   .6285236   .2798536     2.25   0.025     .0800207    1.177027
        _cons |    .932098   .4416717     2.11   0.035     .0664373    1.797759
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        976
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1796994   .0476702    -3.77   0.000    -.2731314   -.0862674
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        976
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0665508   .0256753     2.59   0.010     .0162281    .1168736
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==10 

Iteration 0:   log likelihood = -618.50107  
Iteration 1:   log likelihood = -602.91029  
Iteration 2:   log likelihood = -602.65276  
Iteration 3:   log likelihood =  -602.6524  
Iteration 4:   log likelihood =  -602.6524  

Logistic regression                             Number of obs     =        975
                                                LR chi2(5)        =      31.70
                                                Prob > chi2       =     0.0000
Log likelihood =  -602.6524                     Pseudo R2         =     0.0256

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .2538721   .1399175     1.81   0.070    -.0203611    .5281054
          age |    .009363   .0050942     1.84   0.066    -.0006214    .0193475
    schooling |  -.0451973    .041883    -1.08   0.281    -.1272864    .0368918
    1.partyID |  -1.216913   .2975771    -4.09   0.000    -1.800153   -.6336723
   1.opposeID |   .1050777   .1546572     0.68   0.497    -.1980448    .4082002
        _cons |  -1.087066   .2998668    -3.63   0.000    -1.674794   -.4993375
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.2129785   .0384713    -5.54   0.000    -.2883808   -.1375763
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0227112   .0336443     0.68   0.500    -.0432305    .0886529
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.  
. logit betternational  i.female age schooling i.partyID i.opposeID   if ELECID==10 

Iteration 0:   log likelihood = -533.81951  
Iteration 1:   log likelihood = -497.65974  
Iteration 2:   log likelihood = -495.49276  
Iteration 3:   log likelihood = -495.48492  
Iteration 4:   log likelihood = -495.48492  

Logistic regression                             Number of obs     =        975
                                                LR chi2(5)        =      76.67
                                                Prob > chi2       =     0.0000
Log likelihood = -495.48492                     Pseudo R2         =     0.0718

--------------------------------------------------------------------------------
betternational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -.4347583   .1595649    -2.72   0.006    -.7474998   -.1220168
           age |  -.0065848   .0057447    -1.15   0.252    -.0178442    .0046746
     schooling |  -.0026122   .0467934    -0.06   0.955    -.0943257    .0891012
     1.partyID |     1.8501    .233979     7.91   0.000      1.39151     2.30869
    1.opposeID |   .0715238    .185775     0.39   0.700    -.2925885     .435636
         _cons |  -.9208491   .3306878    -2.78   0.005    -1.568985   -.2727128
--------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(betternational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4049412    .052481     7.72   0.000     .3020803    .5078021
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(betternational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0118191    .030842     0.38   0.702    -.0486301    .0722684
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit worsenational  i.female age schooling i.partyID i.opposeID if ELECID==12

Iteration 0:   log likelihood = -2431.8622  
Iteration 1:   log likelihood = -2295.1903  
Iteration 2:   log likelihood = -2287.5063  
Iteration 3:   log likelihood = -2287.4739  
Iteration 4:   log likelihood = -2287.4739  

Logistic regression                             Number of obs     =      4,691
                                                LR chi2(5)        =     288.78
                                                Prob > chi2       =     0.0000
Log likelihood = -2287.4739                     Pseudo R2         =     0.0594

-------------------------------------------------------------------------------
worsenational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .2988428   .0747517     4.00   0.000     .1523322    .4453534
          age |   .0089437   .0028803     3.11   0.002     .0032985    .0145889
    schooling |  -.1673283    .020529    -8.15   0.000    -.2075644   -.1270923
    1.partyID |  -1.464927   .1377071   -10.64   0.000    -1.734828   -1.195026
   1.opposeID |  -.0872907   .0837105    -1.04   0.297    -.2513603     .076779
        _cons |  -1.125313   .1639968    -6.86   0.000    -1.446741   -.8038856
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      4,691
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1751849   .0113742   -15.40   0.000    -.1974779   -.1528918
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      4,691
Model VCE    : OIM

Expression   : Pr(worsenational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0137157   .0130384    -1.05   0.293    -.0392704     .011839
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit betternational  i.female age schooling i.partyID i.opposeID   if ELECID==12

Iteration 0:   log likelihood = -2941.2644  
Iteration 1:   log likelihood = -2728.9167  
Iteration 2:   log likelihood = -2726.5502  
Iteration 3:   log likelihood = -2726.5497  
Iteration 4:   log likelihood = -2726.5497  

Logistic regression                             Number of obs     =      4,687
                                                LR chi2(5)        =     429.43
                                                Prob > chi2       =     0.0000
Log likelihood = -2726.5497                     Pseudo R2         =     0.0730

--------------------------------------------------------------------------------
betternational |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -.4543415   .0669651    -6.78   0.000    -.5855906   -.3230923
           age |  -.0072529   .0025549    -2.84   0.005    -.0122604   -.0022455
     schooling |   .0947416   .0171337     5.53   0.000     .0611602    .1283229
     1.partyID |   1.388948   .0834895    16.64   0.000     1.225312    1.552585
    1.opposeID |   .3196555   .0789027     4.05   0.000      .165009    .4743021
         _cons |  -.9402721   .1458724    -6.45   0.000    -1.226177   -.6543675
--------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      4,687
Model VCE    : OIM

Expression   : Pr(betternational), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .3142828   .0187284    16.78   0.000     .2775757    .3509899
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      4,687
Model VCE    : OIM

Expression   : Pr(betternational), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0638509   .0158467     4.03   0.000     .0327919      .09491
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. ***Subnational Economic Evaluations at time of subnational survey
. 
. *** Paris subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==22

Iteration 0:   log likelihood = -792.22803  
Iteration 1:   log likelihood = -764.92346  
Iteration 2:   log likelihood = -764.77434  
Iteration 3:   log likelihood = -764.77431  

Logistic regression                             Number of obs     =      1,208
                                                LR chi2(5)        =      54.91
                                                Prob > chi2       =     0.0000
Log likelihood = -764.77431                     Pseudo R2         =     0.0347

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.2808809   .1226359    -2.29   0.022    -.5212429   -.0405188
          age |   .0210077   .0041122     5.11   0.000      .012948    .0290674
    schooling |   .1599448   .0534545     2.99   0.003     .0551759    .2647138
    1.partyID |  -.4442812   .1640092    -2.71   0.007    -.7657334   -.1228291
   1.opposeID |   .3636586   .1471806     2.47   0.013     .0751898    .6521273
        _cons |  -2.193727   .3816576    -5.75   0.000    -2.941762   -1.445692
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      1,208
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0952657   .0336162    -2.83   0.005    -.1611522   -.0293792
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      1,208
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0828198   .0341863     2.42   0.015     .0158159    .1498237
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Marseille subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==23

Iteration 0:   log likelihood = -500.59274  
Iteration 1:   log likelihood = -488.39602  
Iteration 2:   log likelihood = -488.36587  
Iteration 3:   log likelihood = -488.36587  

Logistic regression                             Number of obs     =        725
                                                LR chi2(5)        =      24.45
                                                Prob > chi2       =     0.0002
Log likelihood = -488.36587                     Pseudo R2         =     0.0244

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |  -.1329154   .1541207    -0.86   0.388    -.4349864    .1691555
          age |    .012433   .0053859     2.31   0.021     .0018769    .0229891
    schooling |   -.107366   .0603932    -1.78   0.075    -.2257345    .0110024
    1.partyID |  -.8036419   .2427952    -3.31   0.001    -1.279512   -.3277721
   1.opposeID |   .0814543   .1713948     0.48   0.635    -.2544733     .417382
        _cons |  -.0740126   .4282677    -0.17   0.863    -.9134018    .7653766
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        725
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1863315   .0516589    -3.61   0.000    -.2875811   -.0850819
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        725
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0196441   .0414346     0.47   0.635    -.0615662    .1008543
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==8

Iteration 0:   log likelihood = -425.00412  
Iteration 1:   log likelihood = -409.86729  
Iteration 2:   log likelihood = -409.17422  
Iteration 3:   log likelihood = -409.17311  
Iteration 4:   log likelihood = -409.17311  

Logistic regression                             Number of obs     =        993
                                                LR chi2(5)        =      31.66
                                                Prob > chi2       =     0.0000
Log likelihood = -409.17311                     Pseudo R2         =     0.0372

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .4193548   .1813194     2.31   0.021     .0639753    .7747343
          age |   .0183652   .0064423     2.85   0.004     .0057385    .0309919
    schooling |   .0634995    .078985     0.80   0.421    -.0913081    .2183072
    1.partyID |  -.7981373   .2405519    -3.32   0.001     -1.26961   -.3266643
   1.opposeID |   .4711212   .2279685     2.07   0.039     .0243111    .9179312
        _cons |   .5682915   .3533871     1.61   0.108    -.1243345    1.260917
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        993
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1199557   .0418519    -2.87   0.004    -.2019838   -.0379275
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        993
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0550727   .0246781     2.23   0.026     .0067045    .1034409
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational no data
. 
. *** Lower Saxony subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==11

Iteration 0:   log likelihood =  -564.7949  
Iteration 1:   log likelihood = -546.02253  
Iteration 2:   log likelihood = -545.42696  
Iteration 3:   log likelihood =  -545.4244  
Iteration 4:   log likelihood =  -545.4244  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =      38.74
                                                Prob > chi2       =     0.0000
Log likelihood =  -545.4244                     Pseudo R2         =     0.0343

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |    .354248   .1520616     2.33   0.020     .0562127    .6522833
          age |   .0066205   .0053712     1.23   0.218    -.0039068    .0171478
    schooling |  -.1131146   .0457315    -2.47   0.013    -.2027466   -.0234825
    1.partyID |  -1.298863   .3193997    -4.07   0.000    -1.924875   -.6728506
   1.opposeID |  -.0834941   .1837245    -0.45   0.650    -.4435874    .2765993
        _cons |  -1.080435    .332285    -3.25   0.001    -1.731702   -.4291683
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1850886   .0315663    -5.86   0.000    -.2469574   -.1232198
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0153891   .0335143    -0.46   0.646    -.0810759    .0502976
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.  
. logit  betterregional  i.female age schooling i.partyID i.opposeID if ELECID==11

Iteration 0:   log likelihood = -431.73089  
Iteration 1:   log likelihood = -389.28534  
Iteration 2:   log likelihood = -380.48127  
Iteration 3:   log likelihood = -380.44209  
Iteration 4:   log likelihood = -380.44209  

Logistic regression                             Number of obs     =        983
                                                LR chi2(5)        =     102.58
                                                Prob > chi2       =     0.0000
Log likelihood = -380.44209                     Pseudo R2         =     0.1188

--------------------------------------------------------------------------------
betterregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |  -1.038794   .1978914    -5.25   0.000    -1.426653   -.6509336
           age |   .0014583   .0064063     0.23   0.820    -.0110979    .0140145
     schooling |    .022863   .0545565     0.42   0.675    -.0840658    .1297919
     1.partyID |    1.76649   .2262581     7.81   0.000     1.323032    2.209948
    1.opposeID |  -.2481862   .2532359    -0.98   0.327    -.7445195    .2481471
         _cons |  -1.649553   .3892201    -4.24   0.000    -2.412411   -.8866958
--------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(betterregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .3034171   .0470584     6.45   0.000     .2111843    .3956499
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        983
Model VCE    : OIM

Expression   : Pr(betterregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0281171   .0276313    -1.02   0.309    -.0822734    .0260392
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit worseregional  i.female age schooling i.partyID i.opposeID if ELECID==13

Iteration 0:   log likelihood =  -2026.242  
Iteration 1:   log likelihood = -1926.3241  
Iteration 2:   log likelihood =  -1916.903  
Iteration 3:   log likelihood = -1916.8294  
Iteration 4:   log likelihood = -1916.8294  

Logistic regression                             Number of obs     =      5,906
                                                LR chi2(5)        =     218.83
                                                Prob > chi2       =     0.0000
Log likelihood = -1916.8294                     Pseudo R2         =     0.0540

-------------------------------------------------------------------------------
worseregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     1.female |   .4346712   .0884708     4.91   0.000     .2612716    .6080709
          age |   .0029176   .0033282     0.88   0.381    -.0036056    .0094408
    schooling |  -.1497546   .0241288    -6.21   0.000    -.1970462    -.102463
    1.partyID |  -1.464854   .1622692    -9.03   0.000    -1.782896   -1.146812
   1.opposeID |   .1132831   .1091807     1.04   0.299    -.1007071    .3272732
        _cons |  -1.832346   .1908318    -9.60   0.000    -2.206369   -1.458322
-------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      5,906
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0960598   .0071661   -13.40   0.000    -.1101052   -.0820145
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      5,906
Model VCE    : OIM

Expression   : Pr(worseregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0108529   .0107242     1.01   0.312    -.0101662    .0318721
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit betterregional  i.female age schooling i.partyID i.opposeID if ELECID==13

Iteration 0:   log likelihood = -3620.4001  
Iteration 1:   log likelihood = -3299.2934  
Iteration 2:   log likelihood = -3295.5932  
Iteration 3:   log likelihood = -3295.5917  
Iteration 4:   log likelihood = -3295.5917  

Logistic regression                             Number of obs     =      5,745
                                                LR chi2(5)        =     649.62
                                                Prob > chi2       =     0.0000
Log likelihood = -3295.5917                     Pseudo R2         =     0.0897

--------------------------------------------------------------------------------
betterregional |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      1.female |    -.66208   .0610442   -10.85   0.000    -.7817244   -.5424356
           age |  -.0004427   .0022951    -0.19   0.847    -.0049411    .0040556
     schooling |   .0696996   .0155876     4.47   0.000     .0391485    .1002507
     1.partyID |   1.298024   .0685709    18.93   0.000     1.163627     1.43242
    1.opposeID |  -.2718367   .0898211    -3.03   0.002    -.4478828   -.0957907
         _cons |  -.9507966   .1319523    -7.21   0.000    -1.209418   -.6921748
--------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      5,745
Model VCE    : OIM

Expression   : Pr(betterregional), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .2902558   .0156553    18.54   0.000     .2595719    .3209398
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      5,745
Model VCE    : OIM

Expression   : Pr(betterregional), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0515274   .0165674    -3.11   0.002     -.083999   -.0190559
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. **************************************************************
. 
. **** EXPLAINING ATTRIBUTIONS OF RESPONSIBILITY Table 7.2
. 
. **** National Attributions
. 
. *** IDF national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==5 & NATworse<2

Iteration 0:   log likelihood = -352.45263  
Iteration 1:   log likelihood = -302.95011  
Iteration 2:   log likelihood = -302.30303  
Iteration 3:   log likelihood = -302.29903  
Iteration 4:   log likelihood = -302.29903  

Logistic regression                             Number of obs     =        553
                                                LR chi2(4)        =     100.31
                                                Prob > chi2       =     0.0000
Log likelihood = -302.29903                     Pseudo R2         =     0.1423

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -1.720466   .3227472    -5.33   0.000    -2.353039   -1.087893
       1.opposeID |   1.171927   .2227288     5.26   0.000     .7353862    1.608467
   1.nationalmore |  -.4379638   .2068728    -2.12   0.034    -.8434271   -.0325006
1.highinformation |  -.2786325   .2281476    -1.22   0.222    -.7257936    .1685287
            _cons |   .9449305   .2218762     4.26   0.000      .510061      1.3798
-----------------------------------------------------------------------------------

. 
. *** Provence national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==6 & NATworse<2 

Iteration 0:   log likelihood = -343.96431  
Iteration 1:   log likelihood = -300.00939  
Iteration 2:   log likelihood =  -299.8738  
Iteration 3:   log likelihood = -299.87361  
Iteration 4:   log likelihood = -299.87361  

Logistic regression                             Number of obs     =        521
                                                LR chi2(4)        =      88.18
                                                Prob > chi2       =     0.0000
Log likelihood = -299.87361                     Pseudo R2         =     0.1282

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -1.788372   .3306266    -5.41   0.000    -2.436388   -1.140356
       1.opposeID |   .9359307   .2142668     4.37   0.000     .5159755    1.355886
   1.nationalmore |  -.4031274   .1993061    -2.02   0.043    -.7937602   -.0124946
1.highinformation |    -.09601   .2216047    -0.43   0.665    -.5303472    .3383271
            _cons |   .6478139   .2190839     2.96   0.003     .2184174     1.07721
-----------------------------------------------------------------------------------

. 
. *** Catalonia national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==7 & NATworse<2 

Iteration 0:   log likelihood = -412.43109  
Iteration 1:   log likelihood = -404.79561  
Iteration 2:   log likelihood = -404.69191  
Iteration 3:   log likelihood = -404.69182  
Iteration 4:   log likelihood = -404.69182  

Logistic regression                             Number of obs     =        769
                                                LR chi2(4)        =      15.48
                                                Prob > chi2       =     0.0038
Log likelihood = -404.69182                     Pseudo R2         =     0.0188

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.2614451   .2982605    -0.88   0.381     -.846025    .3231347
       1.opposeID |   .6366151   .3302959     1.93   0.054    -.0107531    1.283983
   1.nationalmore |  -.0893301   .2777846    -0.32   0.748     -.633778    .4551177
1.highinformation |  -.5728704   .1810118    -3.16   0.002     -.927647   -.2180939
            _cons |     1.5352   .1515577    10.13   0.000     1.238152    1.832247
-----------------------------------------------------------------------------------

. 
. *** Madrid national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==9 & NATworse<2 

Iteration 0:   log likelihood =  -404.3766  
Iteration 1:   log likelihood =  -372.1737  
Iteration 2:   log likelihood = -367.34547  
Iteration 3:   log likelihood = -367.08738  
Iteration 4:   log likelihood = -367.08731  
Iteration 5:   log likelihood = -367.08731  

Logistic regression                             Number of obs     =        825
                                                LR chi2(4)        =      74.58
                                                Prob > chi2       =     0.0000
Log likelihood = -367.08731                     Pseudo R2         =     0.0922

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -2.022181   .2789546    -7.25   0.000    -2.568922    -1.47544
       1.opposeID |   .6461939   .2543243     2.54   0.011     .1477273     1.14466
   1.nationalmore |  -.1396627   .2213885    -0.63   0.528    -.5735761    .2942508
1.highinformation |  -.3494337    .206748    -1.69   0.091    -.7546523    .0557848
            _cons |   1.809541   .1848166     9.79   0.000     1.447307    2.171775
-----------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==10 & NATworse<2 

Iteration 0:   log likelihood = -126.92814  
Iteration 1:   log likelihood = -124.30954  
Iteration 2:   log likelihood =  -124.2504  
Iteration 3:   log likelihood = -124.25035  
Iteration 4:   log likelihood = -124.25035  

Logistic regression                             Number of obs     =        312
                                                LR chi2(4)        =       5.36
                                                Prob > chi2       =     0.2527
Log likelihood = -124.25035                     Pseudo R2         =     0.0211

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.1673331   .6807917    -0.25   0.806     -1.50166    1.166994
       1.opposeID |   .8032717   .4089153     1.96   0.049     .0018125    1.604731
   1.nationalmore |   .1657204    .422592     0.39   0.695    -.6625447    .9939856
1.highinformation |  -.4718253   .3472255    -1.36   0.174    -1.152375    .2087241
            _cons |   1.791879   .2571448     6.97   0.000     1.287884    2.295874
-----------------------------------------------------------------------------------

. 
. *** Bavaria national
. logit NATworse i.partyID i.opposeID i.nationalmore  i.highinformation if ELECID==12 & NATworse<2 

Iteration 0:   log likelihood = -487.89236  
Iteration 1:   log likelihood = -479.63911  
Iteration 2:   log likelihood = -479.27932  
Iteration 3:   log likelihood = -479.27918  
Iteration 4:   log likelihood = -479.27918  

Logistic regression                             Number of obs     =        975
                                                LR chi2(4)        =      17.23
                                                Prob > chi2       =     0.0017
Log likelihood = -479.27918                     Pseudo R2         =     0.0177

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.7749347   .2803498    -2.76   0.006     -1.32441   -.2254592
       1.opposeID |   .3346943   .1966592     1.70   0.089    -.0507507    .7201393
   1.nationalmore |   .1126228   .2218139     0.51   0.612    -.3221245      .54737
1.highinformation |  -.3925906   .1724771    -2.28   0.023    -.7306395   -.0545417
            _cons |   1.525803   .1245505    12.25   0.000     1.281688    1.769918
-----------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit NATbetter i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==10 & NATbetter<2 

Iteration 0:   log likelihood = -141.78205  
Iteration 1:   log likelihood = -132.82771  
Iteration 2:   log likelihood = -132.54478  
Iteration 3:   log likelihood = -132.54389  
Iteration 4:   log likelihood = -132.54389  

Logistic regression                             Number of obs     =        229
                                                LR chi2(4)        =      18.48
                                                Prob > chi2       =     0.0010
Log likelihood = -132.54389                     Pseudo R2         =     0.0652

-----------------------------------------------------------------------------------
        NATbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.460624   .4571397     3.20   0.001     .5646464    2.356601
       1.opposeID |  -.3585522    .346675    -1.03   0.301    -1.038023    .3209183
   1.nationalmore |  -.1766497   .3439012    -0.51   0.607    -.8506836    .4973842
1.highinformation |  -.0494539   .3155718    -0.16   0.875    -.6679632    .5690554
            _cons |   .6838011   .2539665     2.69   0.007     .1860359    1.181566
-----------------------------------------------------------------------------------

. 
. 
. *** Bavaria national
. logit NATbetter i.partyID i.opposeID i.nationalmore  i.highinformation if ELECID==12 & NATbetter<2 

Iteration 0:   log likelihood = -819.18657  
Iteration 1:   log likelihood = -766.23753  
Iteration 2:   log likelihood = -763.91707  
Iteration 3:   log likelihood = -763.90955  
Iteration 4:   log likelihood = -763.90955  

Logistic regression                             Number of obs     =      1,494
                                                LR chi2(4)        =     110.55
                                                Prob > chi2       =     0.0000
Log likelihood = -763.90955                     Pseudo R2         =     0.0675

-----------------------------------------------------------------------------------
        NATbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.549328   .1777882     8.71   0.000      1.20087    1.897787
       1.opposeID |   .1856245   .1437956     1.29   0.197    -.0962097    .4674588
   1.nationalmore |  -.2489214   .1552876    -1.60   0.109    -.5532796    .0554367
1.highinformation |  -.0026904   .1358605    -0.02   0.984     -.268972    .2635913
            _cons |   .7533177   .1232575     6.11   0.000     .5117375     .994898
-----------------------------------------------------------------------------------

. 
. 
. ***** Subnational Attributions
. 
. *** Catalonia subnational
. logit REGworse partyID opposeID i.regionmore i.highinformation if ELECID==8 & REGworse<2

Iteration 0:   log likelihood = -520.57407  
Iteration 1:   log likelihood = -475.40283  
Iteration 2:   log likelihood = -474.58326  
Iteration 3:   log likelihood = -474.58165  
Iteration 4:   log likelihood = -474.58165  

Logistic regression                             Number of obs     =        828
                                                LR chi2(4)        =      91.98
                                                Prob > chi2       =     0.0000
Log likelihood = -474.58165                     Pseudo R2         =     0.0883

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
          partyID |  -1.061868   .2444538    -4.34   0.000    -1.540989   -.5827477
         opposeID |    .156321   .1789002     0.87   0.382     -.194317     .506959
     1.regionmore |  -1.074477   .1795744    -5.98   0.000    -1.426436   -.7225176
1.highinformation |  -.4094272   .1618879    -2.53   0.011    -.7267216   -.0921328
            _cons |   1.737175   .1644563    10.56   0.000     1.414846    2.059503
-----------------------------------------------------------------------------------

. 
. 
. *** Madrid subnational MISSING
. 
. *** Lower Saxony subnational
. logit REGworse i.partyID i.opposeID i.regionmore i.highinformation if ELECID==11 & REGworse<2 

Iteration 0:   log likelihood = -125.10061  
Iteration 1:   log likelihood = -121.40756  
Iteration 2:   log likelihood = -121.27511  
Iteration 3:   log likelihood = -121.27469  
Iteration 4:   log likelihood = -121.27469  

Logistic regression                             Number of obs     =        250
                                                LR chi2(4)        =       7.65
                                                Prob > chi2       =     0.1052
Log likelihood = -121.27469                     Pseudo R2         =     0.0306

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.6156526   .6564249    -0.94   0.348    -1.902222    .6709166
       1.opposeID |    .877886   .4718662     1.86   0.063    -.0469548    1.802727
     1.regionmore |  -.5000646   .3334447    -1.50   0.134    -1.153604    .1534751
1.highinformation |   .1435198   .3540508     0.41   0.685     -.550407    .8374467
            _cons |   1.388695    .233779     5.94   0.000     .9304965    1.846893
-----------------------------------------------------------------------------------

. 
. 
. *** Bavaria subnational
. logit REGworse i.partyID i.opposeID i.regionmore i.highinformation if ELECID==13 & REGworse<2

Iteration 0:   log likelihood = -352.28103  
Iteration 1:   log likelihood = -350.50326  
Iteration 2:   log likelihood = -350.49103  
Iteration 3:   log likelihood = -350.49103  

Logistic regression                             Number of obs     =        625
                                                LR chi2(4)        =       3.58
                                                Prob > chi2       =     0.4658
Log likelihood = -350.49103                     Pseudo R2         =     0.0051

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3757455   .3403685    -1.10   0.270    -1.042855    .2913645
       1.opposeID |   .2597229   .2449032     1.06   0.289    -.2202787    .7397244
     1.regionmore |  -.0357832    .186879    -0.19   0.848    -.4020593    .3304929
1.highinformation |  -.1693563   .1952796    -0.87   0.386    -.5520973    .2133846
            _cons |   1.149824   .1511776     7.61   0.000     .8535217    1.446127
-----------------------------------------------------------------------------------

. 
. 
. *** Lower Saxony subnational
. logit REGbetter i.partyID i.opposeID i.regionmore i.highinformation if ELECID==11 & REGbetter<2 

Iteration 0:   log likelihood = -102.85965  
Iteration 1:   log likelihood = -90.667924  
Iteration 2:   log likelihood = -90.355548  
Iteration 3:   log likelihood = -90.354394  
Iteration 4:   log likelihood = -90.354394  

Logistic regression                             Number of obs     =        157
                                                LR chi2(4)        =      25.01
                                                Prob > chi2       =     0.0001
Log likelihood = -90.354394                     Pseudo R2         =     0.1216

-----------------------------------------------------------------------------------
        REGbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.887787   .4773698     3.95   0.000     .9521591    2.823414
       1.opposeID |  -.1144633   .4740582    -0.24   0.809      -1.0436    .8146736
     1.regionmore |  -.4464483   .3929545    -1.14   0.256    -1.216625    .3237283
1.highinformation |  -.1614154   .3731446    -0.43   0.665    -.8927653    .5699346
            _cons |   .2676008   .3127253     0.86   0.392    -.3453295    .8805311
-----------------------------------------------------------------------------------

. 
. 
. *** Bavaria subnational
. logit REGbetter i.partyID i.opposeID i.regionmore  i.highinformation if ELECID==13 & REGbetter<2 

Iteration 0:   log likelihood = -1245.7816  
Iteration 1:   log likelihood = -1170.1588  
Iteration 2:   log likelihood = -1169.6547  
Iteration 3:   log likelihood = -1169.6545  
Iteration 4:   log likelihood = -1169.6545  

Logistic regression                             Number of obs     =      1,853
                                                LR chi2(4)        =     152.25
                                                Prob > chi2       =     0.0000
Log likelihood = -1169.6545                     Pseudo R2         =     0.0611

-----------------------------------------------------------------------------------
        REGbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.084533   .1087701     9.97   0.000      .871348    1.297719
       1.opposeID |  -.2684978   .1571887    -1.71   0.088     -.576582    .0395863
     1.regionmore |  -.1575858   .0992212    -1.59   0.112    -.3520559    .0368842
1.highinformation |   .3049069   .1008184     3.02   0.002     .1073065    .5025073
            _cons |  -.0770337     .09653    -0.80   0.425    -.2662289    .1121616
-----------------------------------------------------------------------------------

. 
. *** Paris municipal MISSING
. 
. *** Marseille municipal MISSING
. 
. 
. 
. ********************************************************
. *** auxilliary analyses with marginal effects based on analyses in Table 7.2
. 
. *** IDF national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==5 & NATworse<2

Iteration 0:   log likelihood = -352.45263  
Iteration 1:   log likelihood = -302.95011  
Iteration 2:   log likelihood = -302.30303  
Iteration 3:   log likelihood = -302.29903  
Iteration 4:   log likelihood = -302.29903  

Logistic regression                             Number of obs     =        553
                                                LR chi2(4)        =     100.31
                                                Prob > chi2       =     0.0000
Log likelihood = -302.29903                     Pseudo R2         =     0.1423

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -1.720466   .3227472    -5.33   0.000    -2.353039   -1.087893
       1.opposeID |   1.171927   .2227288     5.26   0.000     .7353862    1.608467
   1.nationalmore |  -.4379638   .2068728    -2.12   0.034    -.8434271   -.0325006
1.highinformation |  -.2786325   .2281476    -1.22   0.222    -.7257936    .1685287
            _cons |   .9449305   .2218762     4.26   0.000      .510061      1.3798
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        553
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.3738196   .0689578    -5.42   0.000    -.5089743   -.2386648
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        553
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |    .221785   .0404895     5.48   0.000      .142427     .301143
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        553
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |     -.0501   .0402254    -1.25   0.213    -.1289404    .0287403
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        553
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |  -.0801124   .0375108    -2.14   0.033    -.1536322   -.0065926
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==6 & NATworse<2 

Iteration 0:   log likelihood = -343.96431  
Iteration 1:   log likelihood = -300.00939  
Iteration 2:   log likelihood =  -299.8738  
Iteration 3:   log likelihood = -299.87361  
Iteration 4:   log likelihood = -299.87361  

Logistic regression                             Number of obs     =        521
                                                LR chi2(4)        =      88.18
                                                Prob > chi2       =     0.0000
Log likelihood = -299.87361                     Pseudo R2         =     0.1282

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -1.788372   .3306266    -5.41   0.000    -2.436388   -1.140356
       1.opposeID |   .9359307   .2142668     4.37   0.000     .5159755    1.355886
   1.nationalmore |  -.4031274   .1993061    -2.02   0.043    -.7937602   -.0124946
1.highinformation |    -.09601   .2216047    -0.43   0.665    -.5303472    .3383271
            _cons |   .6478139   .2190839     2.96   0.003     .2184174     1.07721
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        521
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.3975897   .0668168    -5.95   0.000    -.5285482   -.2666312
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        521
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .1910976   .0435199     4.39   0.000        .1058    .2763951
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        521
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0186338   .0428029    -0.44   0.663    -.1025258    .0652583
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        521
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |  -.0793848   .0392627    -2.02   0.043    -.1563382   -.0024313
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==7 & NATworse<2 

Iteration 0:   log likelihood = -412.43109  
Iteration 1:   log likelihood = -404.79561  
Iteration 2:   log likelihood = -404.69191  
Iteration 3:   log likelihood = -404.69182  
Iteration 4:   log likelihood = -404.69182  

Logistic regression                             Number of obs     =        769
                                                LR chi2(4)        =      15.48
                                                Prob > chi2       =     0.0038
Log likelihood = -404.69182                     Pseudo R2         =     0.0188

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.2614451   .2982605    -0.88   0.381     -.846025    .3231347
       1.opposeID |   .6366151   .3302959     1.93   0.054    -.0107531    1.283983
   1.nationalmore |  -.0893301   .2777846    -0.32   0.748     -.633778    .4551177
1.highinformation |  -.5728704   .1810118    -3.16   0.002     -.927647   -.2180939
            _cons |     1.5352   .1515577    10.13   0.000     1.238152    1.832247
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        769
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0476284   .0571644    -0.83   0.405    -.1596687    .0644118
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        769
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0954693   .0420637     2.27   0.023      .013026    .1779126
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        769
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |   -.097038   .0296999    -3.27   0.001    -.1552486   -.0388273
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        769
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |    -.01566     .04951    -0.32   0.752    -.1126978    .0813779
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==9 & NATworse<2 

Iteration 0:   log likelihood =  -404.3766  
Iteration 1:   log likelihood =  -372.1737  
Iteration 2:   log likelihood = -367.34547  
Iteration 3:   log likelihood = -367.08738  
Iteration 4:   log likelihood = -367.08731  
Iteration 5:   log likelihood = -367.08731  

Logistic regression                             Number of obs     =        825
                                                LR chi2(4)        =      74.58
                                                Prob > chi2       =     0.0000
Log likelihood = -367.08731                     Pseudo R2         =     0.0922

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -2.022181   .2789546    -7.25   0.000    -2.568922    -1.47544
       1.opposeID |   .6461939   .2543243     2.54   0.011     .1477273     1.14466
   1.nationalmore |  -.1396627   .2213885    -0.63   0.528    -.5735761    .2942508
1.highinformation |  -.3494337    .206748    -1.69   0.091    -.7546523    .0557848
            _cons |   1.809541   .1848166     9.79   0.000     1.447307    2.171775
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        825
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.4186936   .0645632    -6.49   0.000    -.5452352   -.2921521
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        825
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0820745   .0292319     2.81   0.005     .0247811     .139368
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        825
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0468666   .0268214    -1.75   0.081    -.0994356    .0057023
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        825
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |  -.0196921    .031837    -0.62   0.536    -.0820914    .0427072
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit NATworse i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==10 & NATworse<2 

Iteration 0:   log likelihood = -126.92814  
Iteration 1:   log likelihood = -124.30954  
Iteration 2:   log likelihood =  -124.2504  
Iteration 3:   log likelihood = -124.25035  
Iteration 4:   log likelihood = -124.25035  

Logistic regression                             Number of obs     =        312
                                                LR chi2(4)        =       5.36
                                                Prob > chi2       =     0.2527
Log likelihood = -124.25035                     Pseudo R2         =     0.0211

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.1673331   .6807917    -0.25   0.806     -1.50166    1.166994
       1.opposeID |   .8032717   .4089153     1.96   0.049     .0018125    1.604731
   1.nationalmore |   .1657204    .422592     0.39   0.695    -.6625447    .9939856
1.highinformation |  -.4718253   .3472255    -1.36   0.174    -1.152375    .2087241
            _cons |   1.791879   .2571448     6.97   0.000     1.287884    2.295874
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        312
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0209742   .0896672    -0.23   0.815    -.1967186    .1547702
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        312
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0869092   .0399436     2.18   0.030     .0086212    .1651973
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        312
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0565358   .0417336    -1.35   0.176    -.1383322    .0252607
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        312
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |   .0190677   .0469336     0.41   0.685    -.0729205    .1110559
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit NATworse i.partyID i.opposeID i.nationalmore  i.highinformation if ELECID==12 & NATworse<2 

Iteration 0:   log likelihood = -487.89236  
Iteration 1:   log likelihood = -479.63911  
Iteration 2:   log likelihood = -479.27932  
Iteration 3:   log likelihood = -479.27918  
Iteration 4:   log likelihood = -479.27918  

Logistic regression                             Number of obs     =        975
                                                LR chi2(4)        =      17.23
                                                Prob > chi2       =     0.0017
Log likelihood = -479.27918                     Pseudo R2         =     0.0177

-----------------------------------------------------------------------------------
         NATworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.7749347   .2803498    -2.76   0.006     -1.32441   -.2254592
       1.opposeID |   .3346943   .1966592     1.70   0.089    -.0507507    .7201393
   1.nationalmore |   .1126228   .2218139     0.51   0.612    -.3221245      .54737
1.highinformation |  -.3925906   .1724771    -2.28   0.023    -.7306395   -.0545417
            _cons |   1.525803   .1245505    12.25   0.000     1.281688    1.769918
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1457535   .0605331    -2.41   0.016    -.2643962   -.0271109
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0506457   .0285792     1.77   0.076    -.0053686      .10666
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0621255   .0273888    -2.27   0.023    -.1158066   -.0084444
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        975
Model VCE    : OIM

Expression   : Pr(NATworse), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |   .0172974   .0333267     0.52   0.604    -.0480217    .0826165
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit NATbetter i.partyID i.opposeID i.nationalmore i.highinformation if ELECID==10 & NATbetter<2 

Iteration 0:   log likelihood = -141.78205  
Iteration 1:   log likelihood = -132.82771  
Iteration 2:   log likelihood = -132.54478  
Iteration 3:   log likelihood = -132.54389  
Iteration 4:   log likelihood = -132.54389  

Logistic regression                             Number of obs     =        229
                                                LR chi2(4)        =      18.48
                                                Prob > chi2       =     0.0010
Log likelihood = -132.54389                     Pseudo R2         =     0.0652

-----------------------------------------------------------------------------------
        NATbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.460624   .4571397     3.20   0.001     .5646464    2.356601
       1.opposeID |  -.3585522    .346675    -1.03   0.301    -1.038023    .3209183
   1.nationalmore |  -.1766497   .3439012    -0.51   0.607    -.8506836    .4973842
1.highinformation |  -.0494539   .3155718    -0.16   0.875    -.6679632    .5690554
            _cons |   .6838011   .2539665     2.69   0.007     .1860359    1.181566
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        229
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .2516753    .060899     4.13   0.000     .1323155    .3710352
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        229
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0735986   .0730895    -1.01   0.314    -.2168513    .0696542
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        229
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0098059   .0625349    -0.16   0.875    -.1323721    .1127603
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =        229
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |  -.0354706   .0697438    -0.51   0.611     -.172166    .1012248
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit NATbetter i.partyID i.opposeID i.nationalmore  i.highinformation if ELECID==12 & NATbetter<2 

Iteration 0:   log likelihood = -819.18657  
Iteration 1:   log likelihood = -766.23753  
Iteration 2:   log likelihood = -763.91707  
Iteration 3:   log likelihood = -763.90955  
Iteration 4:   log likelihood = -763.90955  

Logistic regression                             Number of obs     =      1,494
                                                LR chi2(4)        =     110.55
                                                Prob > chi2       =     0.0000
Log likelihood = -763.90955                     Pseudo R2         =     0.0675

-----------------------------------------------------------------------------------
        NATbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.549328   .1777882     8.71   0.000      1.20087    1.897787
       1.opposeID |   .1856245   .1437956     1.29   0.197    -.0962097    .4674588
   1.nationalmore |  -.2489214   .1552876    -1.60   0.109    -.5532796    .0554367
1.highinformation |  -.0026904   .1358605    -0.02   0.984     -.268972    .2635913
            _cons |   .7533177   .1232575     6.11   0.000     .5117375     .994898
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      1,494
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .2287374   .0212794    10.75   0.000     .1870305    .2704442
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      1,494
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0308447   .0234553     1.32   0.188    -.0151267    .0768162
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =      1,494
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0004547   .0229567    -0.02   0.984    -.0454491    .0445397
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(nationalmore)

Average marginal effects                        Number of obs     =      1,494
Model VCE    : OIM

Expression   : Pr(NATbetter), predict()
dy/dx w.r.t. : 1.nationalmore

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.nationalmore |  -.0435634   .0280379    -1.55   0.120    -.0985166    .0113899
--------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. ***** Subnational Attributions
. 
. *** Catalonia subnational
. logit REGworse partyID opposeID i.regionmore i.highinformation if ELECID==8 & REGworse<2

Iteration 0:   log likelihood = -520.57407  
Iteration 1:   log likelihood = -475.40283  
Iteration 2:   log likelihood = -474.58326  
Iteration 3:   log likelihood = -474.58165  
Iteration 4:   log likelihood = -474.58165  

Logistic regression                             Number of obs     =        828
                                                LR chi2(4)        =      91.98
                                                Prob > chi2       =     0.0000
Log likelihood = -474.58165                     Pseudo R2         =     0.0883

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
          partyID |  -1.061868   .2444538    -4.34   0.000    -1.540989   -.5827477
         opposeID |    .156321   .1789002     0.87   0.382     -.194317     .506959
     1.regionmore |  -1.074477   .1795744    -5.98   0.000    -1.426436   -.7225176
1.highinformation |  -.4094272   .1618879    -2.53   0.011    -.7267216   -.0921328
            _cons |   1.737175   .1644563    10.56   0.000     1.414846    2.059503
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        828
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     partyID |   -.206588   .0453803    -4.55   0.000    -.2955317   -.1176444
------------------------------------------------------------------------------

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        828
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    opposeID |   .0304125   .0347482     0.88   0.381    -.0376928    .0985178
------------------------------------------------------------------------------

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        828
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0805363   .0318748    -2.53   0.012    -.1430097   -.0180628
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(regionmore)

Average marginal effects                        Number of obs     =        828
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.regionmore

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.regionmore |  -.2094262   .0327607    -6.39   0.000    -.2736361   -.1452164
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit REGworse i.partyID i.opposeID i.regionmore i.highinformation if ELECID==11 & REGworse<2 

Iteration 0:   log likelihood = -125.10061  
Iteration 1:   log likelihood = -121.40756  
Iteration 2:   log likelihood = -121.27511  
Iteration 3:   log likelihood = -121.27469  
Iteration 4:   log likelihood = -121.27469  

Logistic regression                             Number of obs     =        250
                                                LR chi2(4)        =       7.65
                                                Prob > chi2       =     0.1052
Log likelihood = -121.27469                     Pseudo R2         =     0.0306

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.6156526   .6564249    -0.94   0.348    -1.902222    .6709166
       1.opposeID |    .877886   .4718662     1.86   0.063    -.0469548    1.802727
     1.regionmore |  -.5000646   .3334447    -1.50   0.134    -1.153604    .1534751
1.highinformation |   .1435198   .3540508     0.41   0.685     -.550407    .8374467
            _cons |   1.388695    .233779     5.94   0.000     .9304965    1.846893
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        250
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.1104425   .1324306    -0.83   0.404    -.3700017    .1491167
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        250
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .1168466    .052057     2.24   0.025     .0148169    .2188764
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        250
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |   .0219734   .0534132     0.41   0.681    -.0827146    .1266613
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(regionmore)

Average marginal effects                        Number of obs     =        250
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.regionmore

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.regionmore |  -.0817912   .0567561    -1.44   0.150    -.1930311    .0294486
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit REGworse i.partyID i.opposeID i.regionmore i.highinformation if ELECID==13 & REGworse<2

Iteration 0:   log likelihood = -352.28103  
Iteration 1:   log likelihood = -350.50326  
Iteration 2:   log likelihood = -350.49103  
Iteration 3:   log likelihood = -350.49103  

Logistic regression                             Number of obs     =        625
                                                LR chi2(4)        =       3.58
                                                Prob > chi2       =     0.4658
Log likelihood = -350.49103                     Pseudo R2         =     0.0051

-----------------------------------------------------------------------------------
         REGworse |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3757455   .3403685    -1.10   0.270    -1.042855    .2913645
       1.opposeID |   .2597229   .2449032     1.06   0.289    -.2202787    .7397244
     1.regionmore |  -.0357832    .186879    -0.19   0.848    -.4020593    .3304929
1.highinformation |  -.1693563   .1952796    -0.87   0.386    -.5520973    .2133846
            _cons |   1.149824   .1511776     7.61   0.000     .8535217    1.446127
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        625
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |  -.0757545   .0732049    -1.03   0.301    -.2192334    .0677245
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        625
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   .0467597    .042282     1.11   0.269    -.0361116     .129631
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        625
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0320299   .0372961    -0.86   0.390    -.1051289    .0410691
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(regionmore)

Average marginal effects                        Number of obs     =        625
Model VCE    : OIM

Expression   : Pr(REGworse), predict()
dy/dx w.r.t. : 1.regionmore

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.regionmore |  -.0066975   .0350082    -0.19   0.848    -.0753122    .0619172
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony subnational
. logit REGbetter i.partyID i.opposeID i.regionmore i.highinformation if ELECID==11 & REGbetter<2 

Iteration 0:   log likelihood = -102.85965  
Iteration 1:   log likelihood = -90.667924  
Iteration 2:   log likelihood = -90.355548  
Iteration 3:   log likelihood = -90.354394  
Iteration 4:   log likelihood = -90.354394  

Logistic regression                             Number of obs     =        157
                                                LR chi2(4)        =      25.01
                                                Prob > chi2       =     0.0001
Log likelihood = -90.354394                     Pseudo R2         =     0.1216

-----------------------------------------------------------------------------------
        REGbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.887787   .4773698     3.95   0.000     .9521591    2.823414
       1.opposeID |  -.1144633   .4740582    -0.24   0.809      -1.0436    .8146736
     1.regionmore |  -.4464483   .3929545    -1.14   0.256    -1.216625    .3237283
1.highinformation |  -.1614154   .3731446    -0.43   0.665    -.8927653    .5699346
            _cons |   .2676008   .3127253     0.86   0.392    -.3453295    .8805311
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        157
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .3611827   .0716834     5.04   0.000     .2206858    .5016795
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        157
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0228303   .0953352    -0.24   0.811    -.2096839    .1640233
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =        157
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |  -.0317981    .073134    -0.43   0.664     -.175138    .1115418
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(regionmore)

Average marginal effects                        Number of obs     =        157
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.regionmore

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.regionmore |  -.0903403   .0803747    -1.12   0.261    -.2478719    .0671913
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit REGbetter i.partyID i.opposeID i.regionmore  i.highinformation if ELECID==13 & REGbetter<2 

Iteration 0:   log likelihood = -1245.7816  
Iteration 1:   log likelihood = -1170.1588  
Iteration 2:   log likelihood = -1169.6547  
Iteration 3:   log likelihood = -1169.6545  
Iteration 4:   log likelihood = -1169.6545  

Logistic regression                             Number of obs     =      1,853
                                                LR chi2(4)        =     152.25
                                                Prob > chi2       =     0.0000
Log likelihood = -1169.6545                     Pseudo R2         =     0.0611

-----------------------------------------------------------------------------------
        REGbetter |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |   1.084533   .1087701     9.97   0.000      .871348    1.297719
       1.opposeID |  -.2684978   .1571887    -1.71   0.088     -.576582    .0395863
     1.regionmore |  -.1575858   .0992212    -1.59   0.112    -.3520559    .0368842
1.highinformation |   .3049069   .1008184     3.02   0.002     .1073065    .5025073
            _cons |  -.0770337     .09653    -0.80   0.425    -.2662289    .1121616
-----------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      1,853
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .2463647   .0233263    10.56   0.000     .2006459    .2920834
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      1,853
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0604708   .0359925    -1.68   0.093    -.1310148    .0100732
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(highinformation)

Average marginal effects                        Number of obs     =      1,853
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.highinformation

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
1.highinformation |   .0680027   .0225919     3.01   0.003     .0237234    .1122819
-----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(regionmore)

Average marginal effects                        Number of obs     =      1,853
Model VCE    : OIM

Expression   : Pr(REGbetter), predict()
dy/dx w.r.t. : 1.regionmore

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.regionmore |  -.0347436    .021833    -1.59   0.112    -.0775354    .0080482
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Paris municipal MISSING
. 
. *** Marseille municipal MISSING
. 
. ***********************************************************************
. 
. **** VOTE MODELS FOR ECONOMIC EVALUATIONS Table 7.3
. 
. 
. *** NATIONAL VOTE
. 
. *** IDF national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==5  & betternational==0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -199.90085  
Iteration 2:   log likelihood = -191.83238  
Iteration 3:   log likelihood = -191.15791  
Iteration 4:   log likelihood = -191.15744  
Iteration 5:   log likelihood = -191.15744  

Logistic regression                             Number of obs     =        537
                                                LR chi2(3)        =     253.84
                                                Prob > chi2       =     0.0000
Log likelihood = -191.15744                     Pseudo R2         =     0.3990

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.7701881   .2906045    -2.65   0.008    -1.339763   -.2006137
      1.partyID |   2.602585   .3373609     7.71   0.000      1.94137      3.2638
     1.opposeID |  -2.729345   .4416793    -6.18   0.000    -3.595021    -1.86367
          _cons |  -.2566726   .2576963    -1.00   0.319     -.761748    .2484029
---------------------------------------------------------------------------------

. 
. *** Provence national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==6  & betternational==0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -241.25809  
Iteration 2:   log likelihood = -239.33306  
Iteration 3:   log likelihood =  -239.3031  
Iteration 4:   log likelihood = -239.30309  

Logistic regression                             Number of obs     =        538
                                                LR chi2(3)        =     183.03
                                                Prob > chi2       =     0.0000
Log likelihood = -239.30309                     Pseudo R2         =     0.2766

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6669597   .2662343    -2.51   0.012    -1.188769   -.1451501
      1.partyID |   2.537044   .3286381     7.72   0.000     1.892925    3.181162
     1.opposeID |   -1.39183   .2756529    -5.05   0.000      -1.9321   -.8515607
          _cons |  -.3388678   .2392614    -1.42   0.157    -.8078116    .1300759
---------------------------------------------------------------------------------

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==7  & betternational==0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -254.90714  
Iteration 2:   log likelihood = -252.10678  
Iteration 3:   log likelihood = -250.90121  
Iteration 4:   log likelihood = -250.89941  
Iteration 5:   log likelihood = -250.89941  

Logistic regression                             Number of obs     =        661
                                                LR chi2(3)        =     150.76
                                                Prob > chi2       =     0.0000
Log likelihood = -250.89941                     Pseudo R2         =     0.2310

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6559716   .3036674    -2.16   0.031    -1.251149   -.0607944
      1.partyID |   3.609909    .402108     8.98   0.000     2.821792    4.398026
     1.opposeID |  -1.417267   .6025102    -2.35   0.019    -2.598165   -.2363688
          _cons |  -1.223303   .2769807    -4.42   0.000    -1.766175   -.6804306
---------------------------------------------------------------------------------

. 
. *** Madrid national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==9  & betternational==0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -257.41047  
Iteration 2:   log likelihood =  -251.1917  
Iteration 3:   log likelihood = -250.39481  
Iteration 4:   log likelihood = -250.39138  
Iteration 5:   log likelihood = -250.39138  

Logistic regression                             Number of obs     =        698
                                                LR chi2(3)        =     217.81
                                                Prob > chi2       =     0.0000
Log likelihood = -250.39138                     Pseudo R2         =     0.3031

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.150594   .2890031    -3.98   0.000    -1.717029   -.5841581
      1.partyID |   3.351518   .3667752     9.14   0.000     2.632652    4.070384
     1.opposeID |  -1.961725   .4722574    -4.15   0.000    -2.887332   -1.036117
          _cons |  -.5816941   .2609011    -2.23   0.026    -1.093051   -.0703372
---------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==10  & betternational==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -200.30175  
Iteration 2:   log likelihood = -196.83509  
Iteration 3:   log likelihood = -196.60506  
Iteration 4:   log likelihood =  -196.6045  
Iteration 5:   log likelihood =  -196.6045  

Logistic regression                             Number of obs     =        478
                                                LR chi2(3)        =     112.63
                                                Prob > chi2       =     0.0000
Log likelihood =  -196.6045                     Pseudo R2         =     0.2227

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6552645   .2701593    -2.43   0.015    -1.184767    -.125762
      1.partyID |   2.831107   .4723111     5.99   0.000     1.905394     3.75682
     1.opposeID |  -1.787734   .3720992    -4.80   0.000    -2.517035   -1.058433
          _cons |  -.8811042   .1693387    -5.20   0.000    -1.213002   -.5492063
---------------------------------------------------------------------------------

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==12  & betternational==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1140.7594  
Iteration 2:   log likelihood = -1140.5706  
Iteration 3:   log likelihood = -1140.5704  
Iteration 4:   log likelihood = -1140.5704  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(3)        =     734.42
                                                Prob > chi2       =     0.0000
Log likelihood = -1140.5704                     Pseudo R2         =     0.2435

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.701186   .1184012    -5.92   0.000    -.9332481   -.4691238
      1.partyID |   3.239672   .2293127    14.13   0.000     2.790228    3.689117
     1.opposeID |  -1.234019   .1234566   -10.00   0.000    -1.475989   -.9920483
          _cons |  -.4122525   .0687253    -6.00   0.000    -.5469516   -.2775534
---------------------------------------------------------------------------------

. 
. *** SUBNATIONAL VOTE
. 
. *** Marseille
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==23 & betterregional==0

Iteration 0:   log likelihood = -214.86706  
Iteration 1:   log likelihood = -171.73873  
Iteration 2:   log likelihood = -170.79644  
Iteration 3:   log likelihood = -170.79475  
Iteration 4:   log likelihood = -170.79475  

Logistic regression                             Number of obs     =        353
                                                LR chi2(3)        =      88.14
                                                Prob > chi2       =     0.0000
Log likelihood = -170.79475                     Pseudo R2         =     0.2051

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.6383507   .2735097    -2.33   0.020     -1.17442   -.1022816
      1.partyID |   2.495693   .4098204     6.09   0.000      1.69246    3.298926
     1.opposeID |  -1.070777   .3244151    -3.30   0.001    -1.706619   -.4349352
          _cons |  -.6532781   .2053108    -3.18   0.001     -1.05568   -.2508763
---------------------------------------------------------------------------------

. 
. *** Paris
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==22  & betterregional==0

Iteration 0:   log likelihood = -433.40336  
Iteration 1:   log likelihood = -372.06888  
Iteration 2:   log likelihood = -371.75524  
Iteration 3:   log likelihood =  -371.7552  
Iteration 4:   log likelihood =  -371.7552  

Logistic regression                             Number of obs     =        629
                                                LR chi2(3)        =     123.30
                                                Prob > chi2       =     0.0000
Log likelihood =  -371.7552                     Pseudo R2         =     0.1422

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |    -1.8628   .1897911    -9.82   0.000    -2.234784   -1.490816
      1.partyID |   .5031786   .2292011     2.20   0.028     .0539528    .9524044
     1.opposeID |  -.2003037   .2173678    -0.92   0.357    -.6263367    .2257294
          _cons |   .4557628   .1328192     3.43   0.001      .195442    .7160837
---------------------------------------------------------------------------------

. 
. *** Catalonia subnational
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==8 & betterregional==0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -231.35426  
Iteration 2:   log likelihood = -228.70299  
Iteration 3:   log likelihood = -228.61164  
Iteration 4:   log likelihood = -228.61149  
Iteration 5:   log likelihood = -228.61149  

Logistic regression                             Number of obs     =        672
                                                LR chi2(3)        =     246.25
                                                Prob > chi2       =     0.0000
Log likelihood = -228.61149                     Pseudo R2         =     0.3501

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3491733   .3413971    -1.02   0.306    -1.018299    .3199527
      1.partyID |   4.140586   .4215389     9.82   0.000     3.314385    4.966787
     1.opposeID |  -.9475732   .3140693    -3.02   0.003    -1.563138   -.3320086
          _cons |  -1.446441    .319059    -4.53   0.000    -2.071786   -.8210974
---------------------------------------------------------------------------------

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony regional
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==11  & betterregional==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -223.03971  
Iteration 2:   log likelihood = -216.64783  
Iteration 3:   log likelihood = -215.73373  
Iteration 4:   log likelihood = -215.72324  
Iteration 5:   log likelihood = -215.72323  

Logistic regression                             Number of obs     =        528
                                                LR chi2(3)        =     134.53
                                                Prob > chi2       =     0.0000
Log likelihood = -215.72323                     Pseudo R2         =     0.2377

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3506475   .2750236    -1.27   0.202    -.8896838    .1883888
      1.partyID |   2.582902   .3928316     6.58   0.000     1.812966    3.352837
     1.opposeID |  -2.806478    .597584    -4.70   0.000    -3.977721   -1.635235
          _cons |  -1.044927   .1492542    -7.00   0.000     -1.33746   -.7523939
---------------------------------------------------------------------------------

. 
. *** Bavaria subnational
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==13  & betterregional==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood =  -1304.251  
Iteration 2:   log likelihood = -1294.0779  
Iteration 3:   log likelihood =  -1293.872  
Iteration 4:   log likelihood = -1293.8718  
Iteration 5:   log likelihood = -1293.8718  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(3)        =     720.17
                                                Prob > chi2       =     0.0000
Log likelihood = -1293.8718                     Pseudo R2         =     0.2177

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.2947193   .1420572    -2.07   0.038    -.5731463   -.0162924
      1.partyID |   2.377631   .1342741    17.71   0.000     2.114459    2.640804
     1.opposeID |  -1.828375   .1832369    -9.98   0.000    -2.187513   -1.469237
          _cons |  -.8304231   .0592346   -14.02   0.000    -.9465208   -.7143253
---------------------------------------------------------------------------------

. 
. *** TAKING ACCOUNT OF RESPONSIBILITY ATTRIBUTIONS
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==5 & betternational==0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood =  -195.0322  
Iteration 2:   log likelihood = -185.64177  
Iteration 3:   log likelihood = -184.94059  
Iteration 4:   log likelihood = -184.93999  
Iteration 5:   log likelihood = -184.93999  

Logistic regression                             Number of obs     =        537
                                                LR chi2(4)        =     266.27
                                                Prob > chi2       =     0.0000
Log likelihood = -184.93999                     Pseudo R2         =     0.4186

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2008826   .3249311    -0.62   0.536    -.8377358    .4359706
          2  |  -1.309149   .3329566    -3.93   0.000    -1.961732   -.6565663
             |
   1.partyID |    2.40384   .3426426     7.02   0.000     1.732272    3.075407
  1.opposeID |  -2.519687   .4467081    -5.64   0.000    -3.395219   -1.644155
       _cons |  -.2374038   .2553754    -0.93   0.353    -.7379303    .2631227
------------------------------------------------------------------------------

. 
. *** Provence national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==6  & betternational==0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -236.13461  
Iteration 2:   log likelihood = -233.55201  
Iteration 3:   log likelihood = -233.50652  
Iteration 4:   log likelihood = -233.50649  
Iteration 5:   log likelihood = -233.50649  

Logistic regression                             Number of obs     =        538
                                                LR chi2(4)        =     194.62
                                                Prob > chi2       =     0.0000
Log likelihood = -233.50649                     Pseudo R2         =     0.2942

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.1688264   .2948213    -0.57   0.567    -.7466655    .4090128
          2  |   -1.12598   .3011722    -3.74   0.000    -1.716266    -.535693
             |
   1.partyID |   2.297274   .3342109     6.87   0.000     1.642232    2.952315
  1.opposeID |  -1.241044    .280737    -4.42   0.000    -1.791278   -.6908096
       _cons |  -.3320846   .2369494    -1.40   0.161    -.7964969    .1323277
------------------------------------------------------------------------------

. 
. *** Catalonia national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==7  & betternational==0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -252.96673  
Iteration 2:   log likelihood = -249.90245  
Iteration 3:   log likelihood = -248.77513  
Iteration 4:   log likelihood = -248.77387  
Iteration 5:   log likelihood = -248.77387  

Logistic regression                             Number of obs     =        661
                                                LR chi2(4)        =     155.01
                                                Prob > chi2       =     0.0000
Log likelihood = -248.77387                     Pseudo R2         =     0.2375

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2363952   .3581368    -0.66   0.509    -.9383304      .46554
          2  |  -.8197194   .3163661    -2.59   0.010    -1.439786   -.1996533
             |
   1.partyID |   3.613694   .4041236     8.94   0.000     2.821626    4.405761
  1.opposeID |  -1.381525   .6034456    -2.29   0.022    -2.564257   -.1987937
       _cons |  -1.224738   .2770183    -4.42   0.000    -1.767684   -.6817923
------------------------------------------------------------------------------

. 
. *** Madrid national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==9  & betternational==0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -249.35679  
Iteration 2:   log likelihood = -242.09257  
Iteration 3:   log likelihood = -241.25852  
Iteration 4:   log likelihood = -241.25712  
Iteration 5:   log likelihood = -241.25712  

Logistic regression                             Number of obs     =        698
                                                LR chi2(4)        =     236.08
                                                Prob > chi2       =     0.0000
Log likelihood = -241.25712                     Pseudo R2         =     0.3285

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2207697   .3475005    -0.64   0.525     -.901858    .4603187
          2  |   -1.47647    .300629    -4.91   0.000    -2.065692   -.8872479
             |
   1.partyID |   3.074856   .3736952     8.23   0.000     2.342427    3.807285
  1.opposeID |  -1.905893   .4747207    -4.01   0.000    -2.836328   -.9754575
       _cons |  -.5607802    .258441    -2.17   0.030    -1.067315   -.0542452
------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==10  & betternational==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -198.75592  
Iteration 2:   log likelihood = -194.96355  
Iteration 3:   log likelihood =  -194.7205  
Iteration 4:   log likelihood = -194.71995  
Iteration 5:   log likelihood = -194.71995  

Logistic regression                             Number of obs     =        478
                                                LR chi2(4)        =     116.40
                                                Prob > chi2       =     0.0000
Log likelihood = -194.71995                     Pseudo R2         =     0.2301

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .1205013   .4561038     0.26   0.792    -.7734458    1.014448
          2  |  -.8838255   .3048241    -2.90   0.004     -1.48127   -.2863812
             |
   1.partyID |   2.865125   .4770379     6.01   0.000     1.930148    3.800102
  1.opposeID |  -1.766266   .3728972    -4.74   0.000    -2.497131   -1.035401
       _cons |  -.8863807   .1696728    -5.22   0.000    -1.218933    -.553828
------------------------------------------------------------------------------

. 
. *** Bavaria national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==12  & betternational==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1124.1797  
Iteration 2:   log likelihood = -1123.8573  
Iteration 3:   log likelihood = -1123.8571  
Iteration 4:   log likelihood = -1123.8571  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(4)        =     767.84
                                                Prob > chi2       =     0.0000
Log likelihood = -1123.8571                     Pseudo R2         =     0.2546

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .3410902   .2065739     1.65   0.099    -.0637873    .7459676
          2  |  -1.034505   .1384743    -7.47   0.000     -1.30591   -.7631005
             |
   1.partyID |   3.233855   .2302594    14.04   0.000     2.782555    3.685156
  1.opposeID |  -1.231238   .1241252    -9.92   0.000    -1.474519   -.9879566
       _cons |  -.4126121   .0688269    -5.99   0.000    -.5475104   -.2777138
------------------------------------------------------------------------------

. 
. 
. **** Subnational Vote
. 
. *** Marseille Missing 
. 
. *** Paris Missing
. 
. *** Catalonia subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==8  & betterregional==0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -217.36031  
Iteration 2:   log likelihood = -212.28487  
Iteration 3:   log likelihood = -211.55433  
Iteration 4:   log likelihood = -211.55332  
Iteration 5:   log likelihood = -211.55332  

Logistic regression                             Number of obs     =        672
                                                LR chi2(4)        =     280.37
                                                Prob > chi2       =     0.0000
Log likelihood = -211.55332                     Pseudo R2         =     0.3985

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .5898714   .3676264     1.60   0.109     -.130663    1.310406
          2  |  -1.037854   .3726663    -2.78   0.005    -1.768267    -.307442
             |
   1.partyID |   3.988228   .4333093     9.20   0.000     3.138958    4.837499
  1.opposeID |   -1.07025   .3233509    -3.31   0.001    -1.704006   -.4364937
       _cons |  -1.414922   .3169576    -4.46   0.000    -2.036148   -.7936968
------------------------------------------------------------------------------

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==11  & betterregional==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -222.42499  
Iteration 2:   log likelihood = -215.92502  
Iteration 3:   log likelihood = -215.00696  
Iteration 4:   log likelihood = -214.99613  
Iteration 5:   log likelihood = -214.99611  

Logistic regression                             Number of obs     =        528
                                                LR chi2(4)        =     135.98
                                                Prob > chi2       =     0.0000
Log likelihood = -214.99611                     Pseudo R2         =     0.2403

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .1856128   .5049962     0.37   0.713    -.8041616    1.175387
          2  |  -.5005848   .3081294    -1.62   0.104    -1.104507    .1033377
             |
   1.partyID |   2.571228   .3933888     6.54   0.000       1.8002    3.342255
  1.opposeID |  -2.793398   .5978617    -4.67   0.000    -3.965186   -1.621611
       _cons |  -1.044222   .1493044    -6.99   0.000    -1.336853   -.7515906
------------------------------------------------------------------------------

. 
. *** Bavaria subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==13 & worseregional<2  & betterregional==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood =  -1303.739  
Iteration 2:   log likelihood = -1293.4935  
Iteration 3:   log likelihood = -1293.2862  
Iteration 4:   log likelihood =  -1293.286  
Iteration 5:   log likelihood =  -1293.286  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(4)        =     721.34
                                                Prob > chi2       =     0.0000
Log likelihood =  -1293.286                     Pseudo R2         =     0.2181

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |  -.0641329   .2519496    -0.25   0.799    -.5579451    .4296793
          2  |  -.3826004   .1654621    -2.31   0.021    -.7069002   -.0583007
             |
   1.partyID |   2.375053    .134306    17.68   0.000     2.111818    2.638288
  1.opposeID |  -1.826831   .1832595    -9.97   0.000    -2.186013   -1.467649
       _cons |  -.8301053   .0592407   -14.01   0.000    -.9462148   -.7139957
------------------------------------------------------------------------------

. 
. 
. ********************************************************
. *** auxilliary analyses with marginal effects based on analyses in Table 7.3
. 
. 
. *** NATIONAL VOTE
. 
. *** IDF national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==5  & betternational==0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -199.90085  
Iteration 2:   log likelihood = -191.83238  
Iteration 3:   log likelihood = -191.15791  
Iteration 4:   log likelihood = -191.15744  
Iteration 5:   log likelihood = -191.15744  

Logistic regression                             Number of obs     =        537
                                                LR chi2(3)        =     253.84
                                                Prob > chi2       =     0.0000
Log likelihood = -191.15744                     Pseudo R2         =     0.3990

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.7701881   .2906045    -2.65   0.008    -1.339763   -.2006137
      1.partyID |   2.602585   .3373609     7.71   0.000      1.94137      3.2638
     1.opposeID |  -2.729345   .4416793    -6.18   0.000    -3.595021    -1.86367
          _cons |  -.2566726   .2576963    -1.00   0.319     -.761748    .2484029
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0929941   .0367936    -2.53   0.011    -.1651083   -.0208799
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |    .406085   .0626347     6.48   0.000     .2833233    .5288468
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.3166409    .039794    -7.96   0.000    -.3946357   -.2386461
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==6  & betternational==0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -241.25809  
Iteration 2:   log likelihood = -239.33306  
Iteration 3:   log likelihood =  -239.3031  
Iteration 4:   log likelihood = -239.30309  

Logistic regression                             Number of obs     =        538
                                                LR chi2(3)        =     183.03
                                                Prob > chi2       =     0.0000
Log likelihood = -239.30309                     Pseudo R2         =     0.2766

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6669597   .2662343    -2.51   0.012    -1.188769   -.1451501
      1.partyID |   2.537044   .3286381     7.72   0.000     1.892925    3.181162
     1.opposeID |   -1.39183   .2756529    -5.05   0.000      -1.9321   -.8515607
          _cons |  -.3388678   .2392614    -1.42   0.157    -.8078116    .1300759
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1015428   .0431399    -2.35   0.019    -.1860954   -.0169901
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5036456   .0643025     7.83   0.000      .377615    .6296762
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2099825   .0415085    -5.06   0.000    -.2913378   -.1286273
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==7  & betternational==0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -254.90714  
Iteration 2:   log likelihood = -252.10678  
Iteration 3:   log likelihood = -250.90121  
Iteration 4:   log likelihood = -250.89941  
Iteration 5:   log likelihood = -250.89941  

Logistic regression                             Number of obs     =        661
                                                LR chi2(3)        =     150.76
                                                Prob > chi2       =     0.0000
Log likelihood = -250.89941                     Pseudo R2         =     0.2310

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6559716   .3036674    -2.16   0.031    -1.251149   -.0607944
      1.partyID |   3.609909    .402108     8.98   0.000     2.821792    4.398026
     1.opposeID |  -1.417267   .6025102    -2.35   0.019    -2.598165   -.2363688
          _cons |  -1.223303   .2769807    -4.42   0.000    -1.766175   -.6804306
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0848237   .0444355    -1.91   0.056    -.1719156    .0022682
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6941876    .056137    12.37   0.000     .5841611     .804214
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1180524   .0367108    -3.22   0.001    -.1900042   -.0461007
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==9  & betternational==0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -257.41047  
Iteration 2:   log likelihood =  -251.1917  
Iteration 3:   log likelihood = -250.39481  
Iteration 4:   log likelihood = -250.39138  
Iteration 5:   log likelihood = -250.39138  

Logistic regression                             Number of obs     =        698
                                                LR chi2(3)        =     217.81
                                                Prob > chi2       =     0.0000
Log likelihood = -250.39138                     Pseudo R2         =     0.3031

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.150594   .2890031    -3.98   0.000    -1.717029   -.5841581
      1.partyID |   3.351518   .3667752     9.14   0.000     2.632652    4.070384
     1.opposeID |  -1.961725   .4722574    -4.15   0.000    -2.887332   -1.036117
          _cons |  -.5816941   .2609011    -2.23   0.026    -1.093051   -.0703372
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1553587    .045789    -3.39   0.001    -.2451035    -.065614
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |    .599238   .0632596     9.47   0.000     .4752515    .7232244
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1675718   .0301601    -5.56   0.000    -.2266845    -.108459
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==10  & betternational==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -200.30175  
Iteration 2:   log likelihood = -196.83509  
Iteration 3:   log likelihood = -196.60506  
Iteration 4:   log likelihood =  -196.6045  
Iteration 5:   log likelihood =  -196.6045  

Logistic regression                             Number of obs     =        478
                                                LR chi2(3)        =     112.63
                                                Prob > chi2       =     0.0000
Log likelihood =  -196.6045                     Pseudo R2         =     0.2227

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6552645   .2701593    -2.43   0.015    -1.184767    -.125762
      1.partyID |   2.831107   .4723111     5.99   0.000     1.905394     3.75682
     1.opposeID |  -1.787734   .3720992    -4.80   0.000    -2.517035   -1.058433
          _cons |  -.8811042   .1693387    -5.20   0.000    -1.213002   -.5492063
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0824544   .0326358    -2.53   0.012    -.1464195   -.0184893
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5284989   .0865529     6.11   0.000     .3588583    .6981395
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2051133   .0350193    -5.86   0.000    -.2737498   -.1364767
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==12  & betternational==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1140.7594  
Iteration 2:   log likelihood = -1140.5706  
Iteration 3:   log likelihood = -1140.5704  
Iteration 4:   log likelihood = -1140.5704  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(3)        =     734.42
                                                Prob > chi2       =     0.0000
Log likelihood = -1140.5704                     Pseudo R2         =     0.2435

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.701186   .1184012    -5.92   0.000    -.9332481   -.4691238
      1.partyID |   3.239672   .2293127    14.13   0.000     2.790228    3.689117
     1.opposeID |  -1.234019   .1234566   -10.00   0.000    -1.475989   -.9920483
          _cons |  -.4122525   .0687253    -6.00   0.000    -.5469516   -.2775534
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.111216   .0177513    -6.27   0.000    -.1460079   -.0764241
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6001606   .0248363    24.16   0.000     .5514823    .6488389
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   -.199006   .0186811   -10.65   0.000    -.2356203   -.1623917
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** SUBNATIONAL VOTE
. 
. *** Marseille
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==23 & betterregional==0

Iteration 0:   log likelihood = -214.86706  
Iteration 1:   log likelihood = -171.73873  
Iteration 2:   log likelihood = -170.79644  
Iteration 3:   log likelihood = -170.79475  
Iteration 4:   log likelihood = -170.79475  

Logistic regression                             Number of obs     =        353
                                                LR chi2(3)        =      88.14
                                                Prob > chi2       =     0.0000
Log likelihood = -170.79475                     Pseudo R2         =     0.2051

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.6383507   .2735097    -2.33   0.020     -1.17442   -.1022816
      1.partyID |   2.495693   .4098204     6.09   0.000      1.69246    3.298926
     1.opposeID |  -1.070777   .3244151    -3.30   0.001    -1.706619   -.4349352
          _cons |  -.6532781   .2053108    -3.18   0.001     -1.05568   -.2508763
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0997768   .0421747    -2.37   0.018    -.1824377   -.0171158
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5190642   .0763313     6.80   0.000     .3694575    .6686708
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1663391   .0482097    -3.45   0.001    -.2608285   -.0718498
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Paris
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==22  & betterregional==0

Iteration 0:   log likelihood = -433.40336  
Iteration 1:   log likelihood = -372.06888  
Iteration 2:   log likelihood = -371.75524  
Iteration 3:   log likelihood =  -371.7552  
Iteration 4:   log likelihood =  -371.7552  

Logistic regression                             Number of obs     =        629
                                                LR chi2(3)        =     123.30
                                                Prob > chi2       =     0.0000
Log likelihood =  -371.7552                     Pseudo R2         =     0.1422

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |    -1.8628   .1897911    -9.82   0.000    -2.234784   -1.490816
      1.partyID |   .5031786   .2292011     2.20   0.028     .0539528    .9524044
     1.opposeID |  -.2003037   .2173678    -0.92   0.357    -.6263367    .2257294
          _cons |   .4557628   .1328192     3.43   0.001      .195442    .7160837
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.4146549   .0358647   -11.56   0.000    -.4849484   -.3443614
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .1027587   .0466913     2.20   0.028     .0112456    .1942719
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0405796   .0440634    -0.92   0.357    -.1269422     .045783
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia subnational
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==8 & betterregional==0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -231.35426  
Iteration 2:   log likelihood = -228.70299  
Iteration 3:   log likelihood = -228.61164  
Iteration 4:   log likelihood = -228.61149  
Iteration 5:   log likelihood = -228.61149  

Logistic regression                             Number of obs     =        672
                                                LR chi2(3)        =     246.25
                                                Prob > chi2       =     0.0000
Log likelihood = -228.61149                     Pseudo R2         =     0.3501

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3491733   .3413971    -1.02   0.306    -1.018299    .3199527
      1.partyID |   4.140586   .4215389     9.82   0.000     3.314385    4.966787
     1.opposeID |  -.9475732   .3140693    -3.02   0.003    -1.563138   -.3320086
          _cons |  -1.446441    .319059    -4.53   0.000    -2.071786   -.8210974
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0367535   .0387372    -0.95   0.343     -.112677      .03917
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |    .758695   .0464827    16.32   0.000     .6675906    .8497994
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   -.087407   .0277079    -3.15   0.002    -.1417135   -.0331005
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony regional
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==11  & betterregional==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -223.03971  
Iteration 2:   log likelihood = -216.64783  
Iteration 3:   log likelihood = -215.73373  
Iteration 4:   log likelihood = -215.72324  
Iteration 5:   log likelihood = -215.72323  

Logistic regression                             Number of obs     =        528
                                                LR chi2(3)        =     134.53
                                                Prob > chi2       =     0.0000
Log likelihood = -215.72323                     Pseudo R2         =     0.2377

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3506475   .2750236    -1.27   0.202    -.8896838    .1883888
      1.partyID |   2.582902   .3928316     6.58   0.000     1.812966    3.352837
     1.opposeID |  -2.806478    .597584    -4.70   0.000    -3.977721   -1.635235
          _cons |  -1.044927   .1492542    -7.00   0.000     -1.33746   -.7523939
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0447093   .0339788    -1.32   0.188    -.1113067     .021888
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4516131    .067534     6.69   0.000     .3192488    .5839774
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2575036   .0300632    -8.57   0.000    -.3164264   -.1985808
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit INCUMBENT i.worseregional i.partyID i.opposeID if ELECID==13  & betterregional==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood =  -1304.251  
Iteration 2:   log likelihood = -1294.0779  
Iteration 3:   log likelihood =  -1293.872  
Iteration 4:   log likelihood = -1293.8718  
Iteration 5:   log likelihood = -1293.8718  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(3)        =     720.17
                                                Prob > chi2       =     0.0000
Log likelihood = -1293.8718                     Pseudo R2         =     0.2177

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.2947193   .1420572    -2.07   0.038    -.5731463   -.0162924
      1.partyID |   2.377631   .1342741    17.71   0.000     2.114459    2.640804
     1.opposeID |  -1.828375   .1832369    -9.98   0.000    -2.187513   -1.469237
          _cons |  -.8304231   .0592346   -14.02   0.000    -.9465208   -.7143253
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0471925   .0221073    -2.13   0.033     -.090522    -.003863
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4862473   .0249341    19.50   0.000     .4373773    .5351173
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   -.260178   .0200318   -12.99   0.000    -.2994396   -.2209164
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** TAKING ACCOUNT OF RESPONSIBILITY ATTRIBUTIONS
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==5 & betternational==0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood =  -195.0322  
Iteration 2:   log likelihood = -185.64177  
Iteration 3:   log likelihood = -184.94059  
Iteration 4:   log likelihood = -184.93999  
Iteration 5:   log likelihood = -184.93999  

Logistic regression                             Number of obs     =        537
                                                LR chi2(4)        =     266.27
                                                Prob > chi2       =     0.0000
Log likelihood = -184.93999                     Pseudo R2         =     0.4186

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2008826   .3249311    -0.62   0.536    -.8377358    .4359706
          2  |  -1.309149   .3329566    -3.93   0.000    -1.961732   -.6565663
             |
   1.partyID |    2.40384   .3426426     7.02   0.000     1.732272    3.075407
  1.opposeID |  -2.519687   .4467081    -5.64   0.000    -3.395219   -1.644155
       _cons |  -.2374038   .2553754    -0.93   0.353    -.7379303    .2631227
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   -.027049   .0437784    -0.62   0.537     -.112853    .0587551
          2  |  -.1562187   .0422966    -3.69   0.000    -.2391186   -.0733188
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .3591898   .0601468     5.97   0.000     .2413043    .4770754
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2822541   .0413488    -6.83   0.000    -.3632963   -.2012118
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==6  & betternational==0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -236.13461  
Iteration 2:   log likelihood = -233.55201  
Iteration 3:   log likelihood = -233.50652  
Iteration 4:   log likelihood = -233.50649  
Iteration 5:   log likelihood = -233.50649  

Logistic regression                             Number of obs     =        538
                                                LR chi2(4)        =     194.62
                                                Prob > chi2       =     0.0000
Log likelihood = -233.50649                     Pseudo R2         =     0.2942

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.1688264   .2948213    -0.57   0.567    -.7466655    .4090128
          2  |   -1.12598   .3011722    -3.74   0.000    -1.716266    -.535693
             |
   1.partyID |   2.297274   .3342109     6.87   0.000     1.642232    2.952315
  1.opposeID |  -1.241044    .280737    -4.42   0.000    -1.791278   -.6908096
       _cons |  -.3320846   .2369494    -1.40   0.161    -.7964969    .1323277
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.0292514   .0511955    -0.57   0.568    -.1295928    .0710899
          2  |  -.1688622   .0487373    -3.46   0.001    -.2643855   -.0733389
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4410838    .068514     6.44   0.000     .3067989    .5753687
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1813685   .0414417    -4.38   0.000    -.2625928   -.1001443
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==7  & betternational==0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -252.96673  
Iteration 2:   log likelihood = -249.90245  
Iteration 3:   log likelihood = -248.77513  
Iteration 4:   log likelihood = -248.77387  
Iteration 5:   log likelihood = -248.77387  

Logistic regression                             Number of obs     =        661
                                                LR chi2(4)        =     155.01
                                                Prob > chi2       =     0.0000
Log likelihood = -248.77387                     Pseudo R2         =     0.2375

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2363952   .3581368    -0.66   0.509    -.9383304      .46554
          2  |  -.8197194   .3163661    -2.59   0.010    -1.439786   -.1996533
             |
   1.partyID |   3.613694   .4041236     8.94   0.000     2.821626    4.405761
  1.opposeID |  -1.381525   .6034456    -2.29   0.022    -2.564257   -.1987937
       _cons |  -1.224738   .2770183    -4.42   0.000    -1.767684   -.6817923
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.0341996   .0525014    -0.65   0.515    -.1371004    .0687012
          2  |  -.1014887   .0449245    -2.26   0.024    -.1895392   -.0134383
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6894265   .0568904    12.12   0.000     .5779235    .8009296
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1151554   .0370001    -3.11   0.002    -.1876741   -.0426366
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==9  & betternational==0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -249.35679  
Iteration 2:   log likelihood = -242.09257  
Iteration 3:   log likelihood = -241.25852  
Iteration 4:   log likelihood = -241.25712  
Iteration 5:   log likelihood = -241.25712  

Logistic regression                             Number of obs     =        698
                                                LR chi2(4)        =     236.08
                                                Prob > chi2       =     0.0000
Log likelihood = -241.25712                     Pseudo R2         =     0.3285

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.2207697   .3475005    -0.64   0.525     -.901858    .4603187
          2  |   -1.47647    .300629    -4.91   0.000    -2.065692   -.8872479
             |
   1.partyID |   3.074856   .3736952     8.23   0.000     2.342427    3.807285
  1.opposeID |  -1.905893   .4747207    -4.01   0.000    -2.836328   -.9754575
       _cons |  -.5607802    .258441    -2.17   0.030    -1.067315   -.0542452
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |  -.0367345   .0579517    -0.63   0.526    -.1503178    .0768487
          2  |  -.1917502   .0469636    -4.08   0.000    -.2837971   -.0997034
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5323983   .0681846     7.81   0.000      .398759    .6660376
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1592636   .0302184    -5.27   0.000    -.2184905   -.1000366
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==10  & betternational==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -198.75592  
Iteration 2:   log likelihood = -194.96355  
Iteration 3:   log likelihood =  -194.7205  
Iteration 4:   log likelihood = -194.71995  
Iteration 5:   log likelihood = -194.71995  

Logistic regression                             Number of obs     =        478
                                                LR chi2(4)        =     116.40
                                                Prob > chi2       =     0.0000
Log likelihood = -194.71995                     Pseudo R2         =     0.2301

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .1205013   .4561038     0.26   0.792    -.7734458    1.014448
          2  |  -.8838255   .3048241    -2.90   0.004     -1.48127   -.2863812
             |
   1.partyID |   2.865125   .4770379     6.01   0.000     1.930148    3.800102
  1.opposeID |  -1.766266   .3728972    -4.74   0.000    -2.497131   -1.035401
       _cons |  -.8863807   .1696728    -5.22   0.000    -1.218933    -.553828
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .0179801   .0693287     0.26   0.795    -.1179016    .1538618
          2  |  -.1051461   .0333812    -3.15   0.002    -.1705721   -.0397201
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5279363   .0858142     6.15   0.000     .3597435    .6961292
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2011811   .0349422    -5.76   0.000    -.2696665   -.1326956
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit INCUMBENT i.NATresp i.partyID i.opposeID if ELECID==12  & betternational==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1124.1797  
Iteration 2:   log likelihood = -1123.8573  
Iteration 3:   log likelihood = -1123.8571  
Iteration 4:   log likelihood = -1123.8571  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(4)        =     767.84
                                                Prob > chi2       =     0.0000
Log likelihood = -1123.8571                     Pseudo R2         =     0.2546

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .3410902   .2065739     1.65   0.099    -.0637873    .7459676
          2  |  -1.034505   .1384743    -7.47   0.000     -1.30591   -.7631005
             |
   1.partyID |   3.233855   .2302594    14.04   0.000     2.782555    3.685156
  1.opposeID |  -1.231238   .1241252    -9.92   0.000    -1.474519   -.9879566
       _cons |  -.4126121   .0688269    -5.99   0.000    -.5475104   -.2777138
------------------------------------------------------------------------------

. margins, dydx(NATresp)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.NATresp 2.NATresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     NATresp |
          1  |   .0632216   .0393677     1.61   0.108    -.0139376    .1403808
          2  |  -.1540894   .0184756    -8.34   0.000    -.1903009   -.1178779
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5918048   .0255408    23.17   0.000     .5417458    .6418638
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1961279   .0185758   -10.56   0.000    -.2325358     -.15972
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. 
. **** Subnational Vote
. 
. *** Marseille Missng 
. 
. *** Paris Missing
. 
. *** Catalonia subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==8  & betterregional==0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -217.36031  
Iteration 2:   log likelihood = -212.28487  
Iteration 3:   log likelihood = -211.55433  
Iteration 4:   log likelihood = -211.55332  
Iteration 5:   log likelihood = -211.55332  

Logistic regression                             Number of obs     =        672
                                                LR chi2(4)        =     280.37
                                                Prob > chi2       =     0.0000
Log likelihood = -211.55332                     Pseudo R2         =     0.3985

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .5898714   .3676264     1.60   0.109     -.130663    1.310406
          2  |  -1.037854   .3726663    -2.78   0.005    -1.768267    -.307442
             |
   1.partyID |   3.988228   .4333093     9.20   0.000     3.138958    4.837499
  1.opposeID |   -1.07025   .3233509    -3.31   0.001    -1.704006   -.4364937
       _cons |  -1.414922   .3169576    -4.46   0.000    -2.036148   -.7936968
------------------------------------------------------------------------------

. margins, dydx(REGresp)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.REGresp 2.REGresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .0806682   .0472497     1.71   0.088    -.0119396     .173276
          2  |  -.0922085    .039336    -2.34   0.019    -.1693056   -.0151114
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6933595   .0601837    11.52   0.000     .5754017    .8113173
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0934221   .0268257    -3.48   0.000    -.1459996   -.0408447
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==11  & betterregional==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -222.42499  
Iteration 2:   log likelihood = -215.92502  
Iteration 3:   log likelihood = -215.00696  
Iteration 4:   log likelihood = -214.99613  
Iteration 5:   log likelihood = -214.99611  

Logistic regression                             Number of obs     =        528
                                                LR chi2(4)        =     135.98
                                                Prob > chi2       =     0.0000
Log likelihood = -214.99611                     Pseudo R2         =     0.2403

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .1856128   .5049962     0.37   0.713    -.8041616    1.175387
          2  |  -.5005848   .3081294    -1.62   0.104    -1.104507    .1033377
             |
   1.partyID |   2.571228   .3933888     6.54   0.000       1.8002    3.342255
  1.opposeID |  -2.793398   .5978617    -4.67   0.000    -3.965186   -1.621611
       _cons |  -1.044222   .1493044    -6.99   0.000    -1.336853   -.7515906
------------------------------------------------------------------------------

. margins, dydx(REGresp)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.REGresp 2.REGresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |   .0263889   .0738638     0.36   0.721    -.1183815    .1711593
          2  |   -.061888   .0359921    -1.72   0.086    -.1324312    .0086551
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |    .448096   .0676651     6.62   0.000     .3154749    .5807172
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2560597   .0301647    -8.49   0.000    -.3151814   -.1969379
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit INCUMBENT i.REGresp i.partyID i.opposeID if ELECID==13 & worseregional<2  & betterregional==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood =  -1303.739  
Iteration 2:   log likelihood = -1293.4935  
Iteration 3:   log likelihood = -1293.2862  
Iteration 4:   log likelihood =  -1293.286  
Iteration 5:   log likelihood =  -1293.286  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(4)        =     721.34
                                                Prob > chi2       =     0.0000
Log likelihood =  -1293.286                     Pseudo R2         =     0.2181

------------------------------------------------------------------------------
   INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |  -.0641329   .2519496    -0.25   0.799    -.5579451    .4296793
          2  |  -.3826004   .1654621    -2.31   0.021    -.7069002   -.0583007
             |
   1.partyID |   2.375053    .134306    17.68   0.000     2.111818    2.638288
  1.opposeID |  -1.826831   .1832595    -9.97   0.000    -2.186013   -1.467649
       _cons |  -.8301053   .0592407   -14.01   0.000    -.9462148   -.7139957
------------------------------------------------------------------------------

. margins, dydx(REGresp)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.REGresp 2.REGresp

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     REGresp |
          1  |  -.0106243   .0414047    -0.26   0.797    -.0917761    .0705275
          2  |  -.0604759   .0250481    -2.41   0.016    -.1095694   -.0113825
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4855201   .0249539    19.46   0.000     .4366113    .5344289
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2599208   .0200409   -12.97   0.000    -.2992002   -.2206413
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. **** Adding Interactions with Knowledge--impact of negative evaluation does not vary by knowledge
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.worsenational##i.highinformation i.partyID i.opposeID if ELECID==5 & betternationa
> l==0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -197.87794  
Iteration 2:   log likelihood = -189.41441  
Iteration 3:   log likelihood = -188.78145  
Iteration 4:   log likelihood = -188.78087  
Iteration 5:   log likelihood = -188.78087  

Logistic regression                             Number of obs     =        537
                                                LR chi2(5)        =     258.59
                                                Prob > chi2       =     0.0000
Log likelihood = -188.78087                     Pseudo R2         =     0.4065

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -.2361878   .5541949    -0.43   0.670     -1.32239    .8500142
            1.highinformation |   1.097593   .5508758     1.99   0.046     .0178966     2.17729
                              |
worsenational#highinformation |
                         1 1  |  -.8082847   .6549221    -1.23   0.217    -2.091908     .475339
                              |
                    1.partyID |   2.640248   .3415858     7.73   0.000     1.970753    3.309744
                   1.opposeID |  -2.774906   .4431566    -6.26   0.000    -3.643477   -1.906335
                        _cons |  -1.012441   .4670088    -2.17   0.030    -1.927762   -.0971206
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4071207   .0624219     6.52   0.000     .2847761    .5294653
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.3180674   .0392436    -8.10   0.000    -.3949835   -.2411513
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |   .3584393   .0330538    10.84   0.000      .293655    .4232235
                           1  |   .2562263   .0166077    15.43   0.000     .2236758    .2887767
                              |
              highinformation |
                           0  |    .240096   .0269377     8.91   0.000     .1872991    .2928928
                           1  |   .2954117   .0169388    17.44   0.000     .2622121    .3286112
                              |
worsenational#highinformation |
                         0 0  |   .2586318   .0501654     5.16   0.000     .1603095    .3569542
                         0 1  |   .3940913   .0407059     9.68   0.000     .3143091    .4738734
                         1 0  |   .2335356   .0322359     7.24   0.000     .1703544    .2967167
                         1 1  |   .2644967   .0193108    13.70   0.000     .2266482    .3023453
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   .1354594   .0643191     2.11   0.035     .0093962    .2615226
                2  |   .0309612   .0375632     0.82   0.410    -.0426613    .1045836
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit INCUMBENT ii.worsenational##i.highinformation  i.partyID i.opposeID if ELECID==6  & betternati
> onal==0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -240.86768  
Iteration 2:   log likelihood = -238.92459  
Iteration 3:   log likelihood = -238.89466  
Iteration 4:   log likelihood = -238.89465  

Logistic regression                             Number of obs     =        538
                                                LR chi2(5)        =     183.85
                                                Prob > chi2       =     0.0000
Log likelihood = -238.89465                     Pseudo R2         =     0.2779

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -.4285793   .4820361    -0.89   0.374    -1.373353    .5161941
            1.highinformation |   .4285791    .491497     0.87   0.383    -.5347374    1.391896
                              |
worsenational#highinformation |
                         1 1  |  -.3619899   .5768452    -0.63   0.530    -1.492586     .768606
                              |
                    1.partyID |   2.509655    .330498     7.59   0.000     1.861891    3.157419
                   1.opposeID |  -1.408565   .2764843    -5.09   0.000    -1.950464   -.8666655
                        _cons |  -.6173646   .4046735    -1.53   0.127     -1.41051    .1757808
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |     .49722   .0651237     7.64   0.000     .3695799      .62486
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2121306   .0414914    -5.11   0.000    -.2934523   -.1308089
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |   .3881782   .0394694     9.83   0.000     .3108197    .4655368
                           1  |   .2822658   .0182117    15.50   0.000     .2465716      .31796
                              |
              highinformation |
                           0  |   .2897269   .0304612     9.51   0.000     .2300241    .3494298
                           1  |   .3118423   .0192814    16.17   0.000     .2740515     .349633
                              |
worsenational#highinformation |
                         0 0  |   .3376877   .0628159     5.38   0.000     .2145709    .4608046
                         0 1  |   .4081578    .048962     8.34   0.000     .3121941    .5041215
                         1 0  |   .2757182   .0352863     7.81   0.000     .2065582    .3448781
                         1 1  |   .2847688    .021289    13.38   0.000     .2430431    .3264945
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   .0704701   .0792181     0.89   0.374    -.0847945    .2257346
                2  |   .0090507   .0412029     0.22   0.826    -.0717055    .0898068
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational##i.highinformation  i.partyID i.opposeID if ELECID==7  & betternatio
> nal==0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood =  -254.1438  
Iteration 2:   log likelihood = -251.21276  
Iteration 3:   log likelihood = -250.04144  
Iteration 4:   log likelihood = -250.03989  
Iteration 5:   log likelihood = -250.03989  

Logistic regression                             Number of obs     =        661
                                                LR chi2(5)        =     152.48
                                                Prob > chi2       =     0.0000
Log likelihood = -250.03989                     Pseudo R2         =     0.2337

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -.5469162   .4282573    -1.28   0.202    -1.386285    .2924527
            1.highinformation |  -.1498059   .5514623    -0.27   0.786    -1.230652    .9310403
                              |
worsenational#highinformation |
                         1 1  |   -.182907   .6088817    -0.30   0.764    -1.376293    1.010479
                              |
                    1.partyID |   3.665716   .4065436     9.02   0.000     2.868906    4.462527
                   1.opposeID |  -1.415739   .6027991    -2.35   0.019    -2.597203   -.2342743
                        _cons |  -1.152509   .3860155    -2.99   0.003    -1.909086   -.3959325
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6993706   .0550974    12.69   0.000     .5913816    .8073596
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1177111   .0366528    -3.21   0.001    -.1895493   -.0458728
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |   .2672547   .0423315     6.31   0.000     .1842866    .3502228
                           1  |    .184343   .0136536    13.50   0.000     .1575826    .2111035
                              |
              highinformation |
                           0  |   .2146243   .0207842    10.33   0.000      .173888    .2553607
                           1  |   .1807695   .0165808    10.90   0.000     .1482718    .2132672
                              |
worsenational#highinformation |
                         0 0  |   .2802184   .0611574     4.58   0.000     .1603522    .4000846
                         0 1  |    .257297   .0581473     4.42   0.000     .1433303    .3712637
                         1 0  |    .204729    .022159     9.24   0.000     .1612981    .2481599
                         1 1  |   .1690813   .0170399     9.92   0.000     .1356836     .202479
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |  -.0229213   .0843544    -0.27   0.786    -.1882528    .1424102
                2  |  -.0356477   .0279813    -1.27   0.203      -.09049    .0191945
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit INCUMBENT i.worsenational##i.highinformation  i.partyID i.opposeID if ELECID==9  & betternatio
> nal==0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -255.79377  
Iteration 2:   log likelihood = -249.47648  
Iteration 3:   log likelihood = -248.67533  
Iteration 4:   log likelihood = -248.67198  
Iteration 5:   log likelihood = -248.67198  

Logistic regression                             Number of obs     =        698
                                                LR chi2(5)        =     221.25
                                                Prob > chi2       =     0.0000
Log likelihood = -248.67198                     Pseudo R2         =     0.3079

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -1.891565    .492522    -3.84   0.000    -2.856891   -.9262398
            1.highinformation |  -.8629071   .5441478    -1.59   0.113    -1.929417     .203603
                              |
worsenational#highinformation |
                         1 1  |   1.127872   .6119361     1.84   0.065    -.0715004    2.327245
                              |
                    1.partyID |   3.368955   .3673011     9.17   0.000     2.649058    4.088852
                   1.opposeID |  -1.984824   .4746255    -4.18   0.000    -2.915073   -1.054575
                        _cons |  -.0203147   .4332813    -0.05   0.963    -.8695304     .828901
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6010848    .063545     9.46   0.000     .4765389    .7256306
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.1693664   .0304418    -5.56   0.000    -.2290312   -.1097016
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |   .3470031    .042835     8.10   0.000     .2630481    .4309581
                           1  |   .1898148   .0130307    14.57   0.000     .1642751    .2153546
                              |
              highinformation |
                           0  |   .2085285   .0208071    10.02   0.000     .1677473    .2493098
                           1  |   .2119496    .015487    13.69   0.000     .1815957    .2423036
                              |
worsenational#highinformation |
                         0 0  |   .4456851   .0821826     5.42   0.000     .2846101      .60676
                         0 1  |   .2964295   .0498389     5.95   0.000     .1987471    .3941119
                         1 0  |   .1724089   .0215691     7.99   0.000     .1301342    .2146836
                         1 1  |   .1985637    .016441    12.08   0.000       .16634    .2307874
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |  -.1492556   .0958559    -1.56   0.119    -.3371296    .0386184
                2  |   .0261548   .0271476     0.96   0.335    -.0270535    .0793631
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational##i.highinformation  i.partyID i.opposeID if ELECID==10  & betternati
> onal==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -199.97322  
Iteration 2:   log likelihood = -196.57907  
Iteration 3:   log likelihood = -196.35558  
Iteration 4:   log likelihood = -196.35502  
Iteration 5:   log likelihood = -196.35502  

Logistic regression                             Number of obs     =        478
                                                LR chi2(5)        =     113.13
                                                Prob > chi2       =     0.0000
Log likelihood = -196.35502                     Pseudo R2         =     0.2237

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -.7728415   .3825436    -2.02   0.043    -1.522613   -.0230698
            1.highinformation |   .0449515   .3162638     0.14   0.887    -.5749142    .6648171
                              |
worsenational#highinformation |
                         1 1  |   .2638498    .540959     0.49   0.626    -.7964104     1.32411
                              |
                    1.partyID |   2.805678    .474343     5.91   0.000     1.875982    3.735373
                   1.opposeID |  -1.832617   .3783571    -4.84   0.000    -2.574184   -1.091051
                        _cons |  -.8970532   .2341366    -3.83   0.000    -1.355952   -.4381539
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .5239194   .0878138     5.97   0.000     .3518076    .6960313
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2103846   .0358599    -5.87   0.000    -.2806688   -.1401004
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |   .2554668   .0228464    11.18   0.000     .2106887    .3002449
                           1  |   .1752517   .0236256     7.42   0.000     .1289464     .221557
                              |
              highinformation |
                           0  |   .2137555   .0231596     9.23   0.000     .1683636    .2591475
                           1  |   .2311796   .0240535     9.61   0.000     .1840356    .2783237
                              |
worsenational#highinformation |
                         0 0  |   .2522379   .0332402     7.59   0.000     .1870884    .3173874
                         0 1  |   .2587738   .0315129     8.21   0.000     .1970097    .3205379
                         1 0  |   .1587147   .0302595     5.25   0.000     .0994071    .2180223
                         1 1  |   .1918836   .0370664     5.18   0.000     .1192348    .2645324
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   .0065359   .0459565     0.14   0.887    -.0835371     .096609
                2  |   .0331688   .0481401     0.69   0.491     -.061184    .1275216
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational##i.highinformation  i.partyID i.opposeID if ELECID==12  & betternati
> onal==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1136.1289  
Iteration 2:   log likelihood = -1135.6762  
Iteration 3:   log likelihood = -1135.6759  
Iteration 4:   log likelihood = -1135.6759  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(5)        =     744.21
                                                Prob > chi2       =     0.0000
Log likelihood = -1135.6759                     Pseudo R2         =     0.2468

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worsenational |  -.7726423   .1650037    -4.68   0.000    -1.096044    -.449241
            1.highinformation |  -.3508665   .1235083    -2.84   0.004    -.5929384   -.1087946
                              |
worsenational#highinformation |
                         1 1  |   .0635037   .2394016     0.27   0.791    -.4057149    .5327224
                              |
                    1.partyID |   3.308659   .2309455    14.33   0.000     2.856014    3.761304
                   1.opposeID |  -1.149178   .1265032    -9.08   0.000     -1.39712   -.9012365
                        _cons |  -.2305635   .0947914    -2.43   0.015    -.4163512   -.0447758
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .6068373   .0242302    25.04   0.000      .559347    .6543276
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |   -.184914   .0192593    -9.60   0.000    -.2226616   -.1471664
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worsenational##highinformation

Predictive margins                              Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worsenational |
                           0  |    .405509   .0106661    38.02   0.000     .3846038    .4264142
                           1  |   .2890706   .0142565    20.28   0.000     .2611284    .3170128
                              |
              highinformation |
                           0  |   .4006647   .0136192    29.42   0.000     .3739716    .4273578
                           1  |   .3453311   .0111609    30.94   0.000     .3234562     .367206
                              |
worsenational#highinformation |
                         0 0  |   .4412356   .0175984    25.07   0.000     .4067433    .4757278
                         0 1  |   .3780526   .0136088    27.78   0.000     .3513798    .4047253
                         1 0  |   .3111194   .0200859    15.49   0.000     .2717518     .350487
                         1 1  |   .2717246   .0199811    13.60   0.000     .2325623    .3108868
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worsenational=(0 1))

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worsenational   =           0

2._at        : worsenational   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   -.063183   .0224545    -2.81   0.005    -.1071929   -.0191731
                2  |  -.0393949   .0283879    -1.39   0.165    -.0950342    .0162445
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. 
. **** Subnational Vote
. 
. *** Marseille Missng 
. 
. *** Paris Missing
. 
. *** Catalonia subnational
. logit INCUMBENT worseregional##highinformation i.partyID i.opposeID if ELECID==8  & betterregional==
> 0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -229.91352  
Iteration 2:   log likelihood = -226.91334  
Iteration 3:   log likelihood = -226.82037  
Iteration 4:   log likelihood = -226.82012  
Iteration 5:   log likelihood = -226.82012  

Logistic regression                             Number of obs     =        672
                                                LR chi2(5)        =     249.84
                                                Prob > chi2       =     0.0000
Log likelihood = -226.82012                     Pseudo R2         =     0.3551

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worseregional |  -.3035904   .4651423    -0.65   0.514    -1.215253    .6080719
            1.highinformation |   .6626022   .6351289     1.04   0.297    -.5822276    1.907432
                              |
worseregional#highinformation |
                         1 1  |  -.2273019    .690163    -0.33   0.742    -1.579997    1.125393
                              |
                    1.partyID |   4.134949   .4229412     9.78   0.000     3.305999    4.963898
                   1.opposeID |  -1.037298   .3188691    -3.25   0.001     -1.66227   -.4123263
                        _cons |  -1.698882    .424059    -4.01   0.000    -2.530022   -.8677413
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .7537682   .0475559    15.85   0.000     .6605605     .846976
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.0953091   .0281253    -3.39   0.001    -.1504337   -.0401846
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worseregional##highinformation

Predictive margins                              Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worseregional |
                           0  |   .2574293   .0390566     6.59   0.000     .1808797    .3339788
                           1  |   .2117718   .0127213    16.65   0.000     .1868384    .2367052
                              |
              highinformation |
                           0  |   .1962202   .0155227    12.64   0.000     .1657964    .2266441
                           1  |   .2421277   .0190757    12.69   0.000     .2047401    .2795154
                              |
worseregional#highinformation |
                         0 0  |   .2199793   .0415703     5.29   0.000     .1385031    .3014556
                         0 1  |   .2994914   .0679583     4.41   0.000     .1662956    .4326873
                         1 0  |    .192445   .0168107    11.45   0.000     .1594966    .2253934
                         1 1  |   .2335185   .0195908    11.92   0.000     .1951212    .2719159
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worseregional=(0 1))

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worseregional   =           0

2._at        : worseregional   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   .0795121   .0795448     1.00   0.318    -.0763929    .2354171
                2  |   .0410735   .0259417     1.58   0.113    -.0097713    .0919183
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT worseregional##highinformation i.partyID i.opposeID if ELECID==11  & betterregional=
> =0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood =  -222.2148  
Iteration 2:   log likelihood = -215.68127  
Iteration 3:   log likelihood = -214.76161  
Iteration 4:   log likelihood = -214.75059  
Iteration 5:   log likelihood = -214.75057  

Logistic regression                             Number of obs     =        528
                                                LR chi2(5)        =     136.47
                                                Prob > chi2       =     0.0000
Log likelihood = -214.75057                     Pseudo R2         =     0.2411

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worseregional |  -.3176494   .3686519    -0.86   0.389    -1.040194    .4048951
            1.highinformation |   .3454365   .2815995     1.23   0.220    -.2064884    .8973613
                              |
worseregional#highinformation |
                         1 1  |  -.0223927   .5593387    -0.04   0.968    -1.118677    1.073891
                              |
                    1.partyID |   2.555206   .3943693     6.48   0.000     1.782257    3.328156
                   1.opposeID |  -2.895043   .6018857    -4.81   0.000    -4.074717   -1.715368
                        _cons |     -1.201   .2014292    -5.96   0.000    -1.595794   -.8062057
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4451641   .0678784     6.56   0.000     .3121249    .5782034
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2640122    .029979    -8.81   0.000    -.3227699   -.2052544
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worseregional##highinformation

Predictive margins                              Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worseregional |
                           0  |   .2391166    .019038    12.56   0.000     .2018029    .2764304
                           1  |   .1974113    .028393     6.95   0.000     .1417621    .2530606
                              |
              highinformation |
                           0  |   .2077008   .0204356    10.16   0.000     .1676477    .2477539
                           1  |   .2525105   .0249855    10.11   0.000     .2035398    .3014812
                              |
worseregional#highinformation |
                         0 0  |   .2186169   .0251206     8.70   0.000     .1693815    .2678524
                         0 1  |   .2661535   .0294909     9.02   0.000     .2083524    .3239545
                         1 0  |   .1805716   .0349585     5.17   0.000     .1120542    .2490891
                         1 1  |   .2193102   .0474358     4.62   0.000     .1263379    .3122826
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worseregional=(0 1))

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worseregional   =           0

2._at        : worseregional   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |   .0475365   .0388222     1.22   0.221    -.0285535    .1236266
                2  |   .0387386   .0589989     0.66   0.511    -.0768972    .1543744
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit INCUMBENT worseregional##highinformation i.partyID i.opposeID if ELECID==13 & worseregional<2 
>  & betterregional==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood = -1302.2597  
Iteration 2:   log likelihood = -1291.9112  
Iteration 3:   log likelihood =  -1291.704  
Iteration 4:   log likelihood = -1291.7038  
Iteration 5:   log likelihood = -1291.7038  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(5)        =     724.51
                                                Prob > chi2       =     0.0000
Log likelihood = -1291.7038                     Pseudo R2         =     0.2190

-----------------------------------------------------------------------------------------------
                    INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
              1.worseregional |  -.2301416   .1796806    -1.28   0.200    -.5823092    .1220259
            1.highinformation |   -.163212   .1059005    -1.54   0.123    -.3707732    .0443492
                              |
worseregional#highinformation |
                         1 1  |  -.2242948   .2946046    -0.76   0.446    -.8017092    .3531196
                              |
                    1.partyID |   2.412516   .1357946    17.77   0.000     2.146364    2.678669
                   1.opposeID |  -1.783779   .1847299    -9.66   0.000    -2.145843   -1.421715
                        _cons |  -.7578717   .0763261    -9.93   0.000    -.9074682   -.6082752
-----------------------------------------------------------------------------------------------

. margins, dydx(partyID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.partyID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.partyID |   .4915308   .0248959    19.74   0.000     .4427357     .540326
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(opposeID)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.opposeID

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  1.opposeID |  -.2542526   .0204814   -12.41   0.000    -.2943954   -.2141098
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins worseregional##highinformation

Predictive margins                              Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()

-----------------------------------------------------------------------------------------------
                              |            Delta-method
                              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                worseregional |
                           0  |    .344881   .0087312    39.50   0.000     .3277681     .361994
                           1  |   .2914929   .0205019    14.22   0.000     .2513099    .3316759
                              |
              highinformation |
                           0  |   .3524665   .0113918    30.94   0.000      .330139    .3747939
                           1  |   .3209503   .0112434    28.55   0.000     .2989137     .342987
                              |
worseregional#highinformation |
                         0 0  |   .3581658   .0126038    28.42   0.000     .3334627    .3828688
                         0 1  |   .3309823   .0121594    27.22   0.000     .3071503    .3548142
                         1 0  |   .3201954    .026241    12.20   0.000      .268764    .3716268
                         1 1  |   .2620736   .0311204     8.42   0.000     .2010788    .3230685
-----------------------------------------------------------------------------------------------

. margins, dydx(highinformation) at(worseregional=(0 1))

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.highinformation

1._at        : worseregional   =           0

2._at        : worseregional   =           1

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.highinformation  |
               _at |
                1  |  -.0271835   .0176064    -1.54   0.123    -.0616914    .0073244
                2  |  -.0581218   .0406509    -1.43   0.153    -.1377961    .0215526
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. **********************************************************************
. 
. 
. *** Corruption
. 
. *** Perceived National Corruption Table 7.4
. 
. *** IDF national
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore i.highinformation i.female age schooling
>  if ELECID==5 

      Source |       SS           df       MS      Number of obs   =       734
-------------+----------------------------------   F(7, 726)       =      4.05
       Model |  30.4695807         7  4.35279724   Prob > F        =    0.0002
    Residual |  780.963662       726  1.07570752   R-squared       =    0.0376
-------------+----------------------------------   Adj R-squared   =    0.0283
       Total |  811.433243       733  1.10700306   Root MSE        =    1.0372

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3044276   .1120366    -2.72   0.007     -.524382   -.0844733
       1.opposeID |   .0864265   .0850466     1.02   0.310    -.0805401    .2533931
   1.nationalmore |   .0277596   .0805475     0.34   0.730    -.1303743    .1858934
1.highinformation |  -.2370117   .0848695    -2.79   0.005    -.4036306   -.0703928
         1.female |   .1956418   .0783002     2.50   0.013     .0419198    .3493637
              age |   -.000787   .0027916    -0.28   0.778    -.0062676    .0046936
        schooling |   .0013552   .0234677     0.06   0.954    -.0447175    .0474279
            _cons |   1.603702   .2029269     7.90   0.000     1.205308    2.002095
-----------------------------------------------------------------------------------

. 
. *** Provence national
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore   i.highinformation i.female age schooli
> ng if ELECID==6 

      Source |       SS           df       MS      Number of obs   =       711
-------------+----------------------------------   F(7, 703)       =      9.03
       Model |  65.4608919         7  9.35155598   Prob > F        =    0.0000
    Residual |  727.858377       703  1.03536042   R-squared       =    0.0825
-------------+----------------------------------   Adj R-squared   =    0.0734
       Total |  793.319269       710  1.11735108   Root MSE        =    1.0175

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.4592547   .1096412    -4.19   0.000    -.6745182   -.2439913
       1.opposeID |  -.1026367   .0859849    -1.19   0.233    -.2714547    .0661812
   1.nationalmore |  -.1352776   .0791148    -1.71   0.088    -.2906071    .0200519
1.highinformation |  -.1253871   .0877613    -1.43   0.154    -.2976928    .0469186
         1.female |   .3767326   .0774104     4.87   0.000     .2247493    .5287159
              age |  -.0001527   .0027005    -0.06   0.955    -.0054547    .0051493
        schooling |  -.0395869   .0231326    -1.71   0.087    -.0850042    .0058305
            _cons |   1.821214    .200388     9.09   0.000     1.427784    2.214645
-----------------------------------------------------------------------------------

. 
. *** Catalonia national
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore  i.highinformation i.female age schoolin
> g if ELECID==7

      Source |       SS           df       MS      Number of obs   =       924
-------------+----------------------------------   F(7, 916)       =      8.00
       Model |  44.1729349         7  6.31041927   Prob > F        =    0.0000
    Residual |  722.121437       916  .788342181   R-squared       =    0.0576
-------------+----------------------------------   Adj R-squared   =    0.0504
       Total |  766.294372       923  .830221422   Root MSE        =    .88789

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.6564353   .1041775    -6.30   0.000    -.8608895    -.451981
       1.opposeID |   .1655419    .098672     1.68   0.094    -.0281076    .3591914
   1.nationalmore |   .1537124   .0906083     1.70   0.090    -.0241116    .3315363
1.highinformation |  -.1296362   .0612199    -2.12   0.034    -.2497838   -.0094886
         1.female |  -.0199248   .0602458    -0.33   0.741    -.1381606     .098311
              age |   .0016067   .0022515     0.71   0.476    -.0028119    .0060254
        schooling |  -.0136258   .0263602    -0.52   0.605    -.0653592    .0381076
            _cons |   2.185282   .1333207    16.39   0.000     1.923632    2.446931
-----------------------------------------------------------------------------------

. 
. *** Madrid national
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore  i.highinformation i.female age schoolin
> g if ELECID==9 

      Source |       SS           df       MS      Number of obs   =       963
-------------+----------------------------------   F(7, 955)       =     15.71
       Model |  80.7286184         7  11.5326598   Prob > F        =    0.0000
    Residual |  700.878858       955  .733904564   R-squared       =    0.1033
-------------+----------------------------------   Adj R-squared   =    0.0967
       Total |  781.607477       962  .812481784   Root MSE        =    .85668

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.8766183   .0936324    -9.36   0.000    -1.060367   -.6928693
       1.opposeID |   .0933721   .0680054     1.37   0.170    -.0400852    .2268294
   1.nationalmore |  -.1215863   .0669114    -1.82   0.070    -.2528966    .0097239
1.highinformation |  -.0716651   .0608683    -1.18   0.239    -.1911161    .0477859
         1.female |  -.0473174   .0564333    -0.84   0.402    -.1580649    .0634301
              age |    -.00119   .0024671    -0.48   0.630    -.0060315    .0036516
        schooling |   .0143483   .0250231     0.57   0.567    -.0347583     .063455
            _cons |   2.221457   .1356416    16.38   0.000     1.955267    2.487647
-----------------------------------------------------------------------------------

. 
. *** Lower Saxony
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore  i.highinformation i.female age schoolin
> g if ELECID==10 

      Source |       SS           df       MS      Number of obs   =       942
-------------+----------------------------------   F(7, 934)       =      2.60
       Model |  22.4782005         7   3.2111715   Prob > F        =    0.0116
    Residual |  1153.61628       934   1.2351352   R-squared       =    0.0191
-------------+----------------------------------   Adj R-squared   =    0.0118
       Total |  1176.09448       941  1.24983473   Root MSE        =    1.1114

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3733674   .1223866    -3.05   0.002    -.6135519   -.1331829
       1.opposeID |   .0804714   .0851027     0.95   0.345    -.0865432     .247486
   1.nationalmore |  -.0030618   .0865302    -0.04   0.972    -.1728779    .1667543
1.highinformation |  -.0331701   .0781151    -0.42   0.671    -.1864715    .1201313
         1.female |   .0387915   .0738569     0.53   0.600    -.1061532    .1837361
              age |   .0064714   .0026874     2.41   0.016     .0011973    .0117454
        schooling |    .028853   .0219274     1.32   0.189    -.0141797    .0718856
            _cons |   1.302623   .1555404     8.37   0.000     .9973737    1.607872
-----------------------------------------------------------------------------------

. 
. *** Bavaria national
. regress nationalcorrupt i.partyID i.opposeID i.nationalmore   i.highinformation i.female age if ELEC
> ID==12

      Source |       SS           df       MS      Number of obs   =     4,594
-------------+----------------------------------   F(6, 4587)      =     22.00
       Model |    133.4189         6  22.2364833   Prob > F        =    0.0000
    Residual |  4635.32664     4,587  1.01053557   R-squared       =    0.0280
-------------+----------------------------------   Adj R-squared   =    0.0267
       Total |  4768.74554     4,593  1.03826378   Root MSE        =    1.0053

-----------------------------------------------------------------------------------
  nationalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3499438   .0401935    -8.71   0.000    -.4287425   -.2711451
       1.opposeID |  -.0739459   .0360475    -2.05   0.040    -.1446164   -.0032754
   1.nationalmore |  -.0822681   .0395358    -2.08   0.038    -.1597773    -.004759
1.highinformation |   -.093724   .0325778    -2.88   0.004    -.1575921   -.0298558
         1.female |   .1193733   .0306609     3.89   0.000     .0592631    .1794835
              age |   .0020253   .0011585     1.75   0.080    -.0002459    .0042964
            _cons |   1.792769    .059233    30.27   0.000     1.676644    1.908894
-----------------------------------------------------------------------------------

. 
. ***Perceived Subnational Corruption 
. 
. *** Paris
. regress municipalcorrupt i.partyID i.opposeID i.highinformation i.female age schooling if ELECID==22

      Source |       SS           df       MS      Number of obs   =     1,208
-------------+----------------------------------   F(6, 1201)      =      3.83
       Model |  27.6042765         6  4.60071274   Prob > F        =    0.0009
    Residual |  1443.26245     1,201  1.20171727   R-squared       =    0.0188
-------------+----------------------------------   Adj R-squared   =    0.0139
       Total |  1470.86672     1,207  1.21861369   Root MSE        =    1.0962

-----------------------------------------------------------------------------------
 municipalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.2886089   .0805641    -3.58   0.000     -.446671   -.1305469
       1.opposeID |   .1405824     .07844     1.79   0.073    -.0133123    .2944771
1.highinformation |   -.061979   .0673957    -0.92   0.358    -.1942053    .0702474
         1.female |  -.0029735   .0635104    -0.05   0.963    -.1275771    .1216302
              age |  -.0016234   .0020999    -0.77   0.440    -.0057433    .0024966
        schooling |   .0128511   .0270336     0.48   0.635    -.0401873    .0658896
            _cons |   1.610363   .1908422     8.44   0.000     1.235942    1.984784
-----------------------------------------------------------------------------------

. 
. *** Marseille
. regress municipalcorrupt i.partyID i.opposeID i.highinformation i.female age schooling if ELECID==23

      Source |       SS           df       MS      Number of obs   =       725
-------------+----------------------------------   F(6, 718)       =      7.41
       Model |  46.1907893         6  7.69846488   Prob > F        =    0.0000
    Residual |  746.200935       718  1.03927707   R-squared       =    0.0583
-------------+----------------------------------   Adj R-squared   =    0.0504
       Total |  792.391724       724  1.09446371   Root MSE        =    1.0194

-----------------------------------------------------------------------------------
 municipalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.4921965   .1154044    -4.26   0.000    -.7187668   -.2656261
       1.opposeID |   .0017642   .0873901     0.02   0.984    -.1698065    .1733348
1.highinformation |   .2144222   .0807936     2.65   0.008     .0558022    .3730422
         1.female |   .0610709   .0783307     0.78   0.436    -.0927138    .2148555
              age |  -.0074001   .0026949    -2.75   0.006     -.012691   -.0021093
        schooling |   .0557657   .0304801     1.83   0.068     -.004075    .1156064
            _cons |   1.963342   .2162382     9.08   0.000     1.538807    2.387877
-----------------------------------------------------------------------------------

. 
. *** Catalonia subnational
. regress regionalcorrupt i.partyID i.opposeID i.regionmore  i.highinformation i.female age schooling 
> if ELECID==8

      Source |       SS           df       MS      Number of obs   =       975
-------------+----------------------------------   F(7, 967)       =     32.72
       Model |  151.006326         7  21.5723323   Prob > F        =    0.0000
    Residual |  637.541366       967  .659298207   R-squared       =    0.1915
-------------+----------------------------------   Adj R-squared   =    0.1856
       Total |  788.547692       974   .80959722   Root MSE        =    .81197

-----------------------------------------------------------------------------------
  regionalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.5926306   .0831297    -7.13   0.000    -.7557661   -.4294952
       1.opposeID |   .0447078   .0604729     0.74   0.460    -.0739653     .163381
     1.regionmore |   -.523369    .056445    -9.27   0.000    -.6341378   -.4126002
1.highinformation |  -.1164548   .0548162    -2.12   0.034    -.2240273   -.0088823
         1.female |  -.0315683   .0526034    -0.60   0.549    -.1347982    .0716616
              age |  -.0055043   .0018599    -2.96   0.003    -.0091541   -.0018545
        schooling |   .0131743   .0232275     0.57   0.571    -.0324077    .0587563
            _cons |   2.640399   .1101034    23.98   0.000      2.42433    2.856468
-----------------------------------------------------------------------------------

. 
. *** Madrid subnational
. regress regionalcorrupt i.partyID i.opposeID i.regionmore  i.highinformation i.female age schooling 
> if ELECID==24

      Source |       SS           df       MS      Number of obs   =       908
-------------+----------------------------------   F(7, 900)       =     31.63
       Model |  132.804036         7  18.9720052   Prob > F        =    0.0000
    Residual |  539.821514       900  .599801683   R-squared       =    0.1974
-------------+----------------------------------   Adj R-squared   =    0.1912
       Total |  672.625551       907  .741593771   Root MSE        =    .77447

-----------------------------------------------------------------------------------
  regionalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -1.113807   .0962874   -11.57   0.000    -1.302781   -.9248326
       1.opposeID |   .2856648   .0635066     4.50   0.000     .1610264    .4103031
     1.regionmore |   .0180809   .0754014     0.24   0.811    -.1299022     .166064
1.highinformation |    .074602   .0527871     1.41   0.158    -.0289981    .1782021
         1.female |   .1410719   .0519203     2.72   0.007     .0391729    .2429709
              age |   -.006622   .0019525    -3.39   0.001    -.0104539   -.0027901
        schooling |  -.0430018    .021734    -1.98   0.048     -.085657   -.0003467
            _cons |   2.746906   .1280504    21.45   0.000     2.495594    2.998218
-----------------------------------------------------------------------------------

. 
. *** Lower Saxony subnational
. regress regionalcorrupt i.partyID i.opposeID i.regionmore  i.highinformation i.female age schooling 
> if ELECID==11

      Source |       SS           df       MS      Number of obs   =       950
-------------+----------------------------------   F(7, 942)       =      4.13
       Model |  30.2196117         7  4.31708739   Prob > F        =    0.0002
    Residual |  985.228809       942  1.04589046   R-squared       =    0.0298
-------------+----------------------------------   Adj R-squared   =    0.0226
       Total |  1015.44842       949  1.07001941   Root MSE        =    1.0227

-----------------------------------------------------------------------------------
  regionalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.3219363   .1036311    -3.11   0.002    -.5253108   -.1185617
       1.opposeID |  -.1101596   .0839758    -1.31   0.190    -.2749609    .0546417
     1.regionmore |   .0550828   .0745933     0.74   0.460    -.0913056    .2014711
1.highinformation |  -.1153254   .0698452    -1.65   0.099    -.2523956    .0217447
         1.female |  -.0547879   .0689082    -0.80   0.427    -.1900192    .0804434
              age |    .009439   .0023876     3.95   0.000     .0047533    .0141247
        schooling |    .041759    .019868     2.10   0.036     .0027683    .0807497
            _cons |    1.03585   .1503221     6.89   0.000     .7408449    1.330855
-----------------------------------------------------------------------------------

. 
. *** Bavaria subnational
. regress regionalcorrupt i.partyID i.opposeID i.regionmore   i.highinformation i.female age schooling
>  if ELECID==13

      Source |       SS           df       MS      Number of obs   =     5,769
-------------+----------------------------------   F(7, 5761)      =     53.58
       Model |  347.535681         7  49.6479545   Prob > F        =    0.0000
    Residual |  5338.35442     5,761  .926636768   R-squared       =    0.0611
-------------+----------------------------------   Adj R-squared   =    0.0600
       Total |   5685.8901     5,768  .985764581   Root MSE        =    .96262

-----------------------------------------------------------------------------------
  regionalcorrupt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        1.partyID |  -.4317461   .0313013   -13.79   0.000    -.4931084   -.3703838
       1.opposeID |   .2234209   .0358884     6.23   0.000     .1530663    .2937756
     1.regionmore |   .0074577   .0260088     0.29   0.774    -.0435294    .0584448
1.highinformation |   .1465618   .0271761     5.39   0.000     .0932865    .1998371
         1.female |   .0598791   .0260153     2.30   0.021     .0088793    .1108788
              age |   .0048359   .0010092     4.79   0.000     .0028574    .0068143
        schooling |   .0216134    .006796     3.18   0.001     .0082907    .0349362
            _cons |   1.364545   .0595368    22.92   0.000      1.24783     1.48126
-----------------------------------------------------------------------------------

. 
. 
. 
. 
. *********************************************************
. 
. **** Examining Impact of Cross-level Economic Evaluations Table 7.5
. 
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==5  & betternational=
> =0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -199.33711  
Iteration 2:   log likelihood = -191.09449  
Iteration 3:   log likelihood = -190.42384  
Iteration 4:   log likelihood = -190.42331  
Iteration 5:   log likelihood = -190.42331  

Logistic regression                             Number of obs     =        537
                                                LR chi2(4)        =     255.30
                                                Prob > chi2       =     0.0000
Log likelihood = -190.42331                     Pseudo R2         =     0.4013

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.8792855   .3063073    -2.87   0.004    -1.479637   -.2789342
1.worseregional |   .3330972   .2751954     1.21   0.226    -.2062759    .8724702
      1.partyID |   2.570303   .3384496     7.59   0.000     1.906954    3.233652
     1.opposeID |  -2.749814   .4425126    -6.21   0.000    -3.617123   -1.882505
          _cons |  -.3084349   .2619905    -1.18   0.239    -.8219269    .2050571
---------------------------------------------------------------------------------

. 
. *** Provence national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==6  & betternational=
> =0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -240.94392  
Iteration 2:   log likelihood = -238.98126  
Iteration 3:   log likelihood = -238.95054  
Iteration 4:   log likelihood = -238.95053  

Logistic regression                             Number of obs     =        538
                                                LR chi2(4)        =     183.74
                                                Prob > chi2       =     0.0000
Log likelihood = -238.95053                     Pseudo R2         =     0.2777

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.7439554   .2822873    -2.64   0.008    -1.297228   -.1906824
1.worseregional |   .2055319   .2452893     0.84   0.402    -.2752263    .6862902
      1.partyID |   2.538938   .3289206     7.72   0.000     1.894266    3.183611
     1.opposeID |  -1.402921   .2762499    -5.08   0.000    -1.944361   -.8614813
          _cons |  -.3757716   .2435507    -1.54   0.123    -.8531223    .1015791
---------------------------------------------------------------------------------

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==7  & betternational=
> =0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -254.85936  
Iteration 2:   log likelihood = -252.05088  
Iteration 3:   log likelihood = -250.85609  
Iteration 4:   log likelihood = -250.85436  
Iteration 5:   log likelihood = -250.85436  

Logistic regression                             Number of obs     =        661
                                                LR chi2(4)        =     150.85
                                                Prob > chi2       =     0.0000
Log likelihood = -250.85436                     Pseudo R2         =     0.2312

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6884096   .3228407    -2.13   0.033    -1.321166   -.0556534
1.worseregional |   .0799382   .2670973     0.30   0.765     -.443563    .6034393
      1.partyID |   3.607272   .4022979     8.97   0.000     2.818782    4.395761
     1.opposeID |  -1.422201   .6027299    -2.36   0.018     -2.60353    -.240872
          _cons |  -1.251115   .2928453    -4.27   0.000    -1.825081   -.6771489
---------------------------------------------------------------------------------

. 
. *** Madrid national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==9  & betternational=
> =0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -253.09884  
Iteration 2:   log likelihood = -245.73988  
Iteration 3:   log likelihood = -244.85522  
Iteration 4:   log likelihood = -244.85005  
Iteration 5:   log likelihood = -244.85005  

Logistic regression                             Number of obs     =        698
                                                LR chi2(4)        =     228.89
                                                Prob > chi2       =     0.0000
Log likelihood = -244.85005                     Pseudo R2         =     0.3185

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.310009   .3001803    -4.36   0.000    -1.898352   -.7216665
1.worseregional |   .8293593   .2563195     3.24   0.001     .3269824    1.331736
      1.partyID |   3.228042   .3710715     8.70   0.000     2.500755    3.955328
     1.opposeID |  -1.855374   .4747925    -3.91   0.000     -2.78595   -.9247976
          _cons |  -.9764774   .2965669    -3.29   0.001    -1.557738    -.395217
---------------------------------------------------------------------------------

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==10  & betternational
> ==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -199.97769  
Iteration 2:   log likelihood = -196.53964  
Iteration 3:   log likelihood = -196.31665  
Iteration 4:   log likelihood = -196.31608  
Iteration 5:   log likelihood = -196.31608  

Logistic regression                             Number of obs     =        478
                                                LR chi2(4)        =     113.21
                                                Prob > chi2       =     0.0000
Log likelihood = -196.31608                     Pseudo R2         =     0.2238

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.5145067   .3253954    -1.58   0.114     -1.15227    .1232566
1.worseregional |   -.268418   .3551333    -0.76   0.450    -.9644664    .4276304
      1.partyID |   2.839093   .4735886     5.99   0.000     1.910876     3.76731
     1.opposeID |   -1.80755    .373177    -4.84   0.000    -2.538963   -1.076136
          _cons |  -.8563224    .172101    -4.98   0.000    -1.193634   -.5190106
---------------------------------------------------------------------------------

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==12  & betternational
> ==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1134.0937  
Iteration 2:   log likelihood =  -1133.965  
Iteration 3:   log likelihood = -1133.9649  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(4)        =     747.63
                                                Prob > chi2       =     0.0000
Log likelihood = -1133.9649                     Pseudo R2         =     0.2479

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6652016   .1190736    -5.59   0.000    -.8985816   -.4318216
1.worseregional |   .4691207   .1279522     3.67   0.000     .2183391    .7199024
      1.partyID |   3.207298   .2296673    13.96   0.000     2.757158    3.657437
     1.opposeID |  -1.247995    .124004   -10.06   0.000    -1.491039   -1.004952
          _cons |  -.5102669   .0742531    -6.87   0.000    -.6558002   -.3647336
---------------------------------------------------------------------------------

. 
. **** Subnational Vote
. 
. *** Marseille
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==23  & betterregional
> ==0

Iteration 0:   log likelihood = -214.86706  
Iteration 1:   log likelihood = -161.39623  
Iteration 2:   log likelihood = -158.95963  
Iteration 3:   log likelihood = -158.88809  
Iteration 4:   log likelihood = -158.88801  
Iteration 5:   log likelihood = -158.88801  

Logistic regression                             Number of obs     =        353
                                                LR chi2(4)        =     111.96
                                                Prob > chi2       =     0.0000
Log likelihood = -158.88801                     Pseudo R2         =     0.2605

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.925759   .4654559     4.14   0.000     1.013482    2.838036
1.worseregional |  -.9573545   .2877867    -3.33   0.001    -1.521406   -.3933029
      1.partyID |    2.17359   .4194022     5.18   0.000     1.351576    2.995603
     1.opposeID |  -.9226439   .3354537    -2.75   0.006    -1.580121   -.2651668
          _cons |  -2.069754   .4427596    -4.67   0.000    -2.937546   -1.201961
---------------------------------------------------------------------------------

. 
. *** Paris
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==22  & betterregional
> ==0

Iteration 0:   log likelihood = -433.40336  
Iteration 1:   log likelihood = -357.54954  
Iteration 2:   log likelihood = -357.36252  
Iteration 3:   log likelihood = -357.36245  
Iteration 4:   log likelihood = -357.36245  

Logistic regression                             Number of obs     =        629
                                                LR chi2(4)        =     152.08
                                                Prob > chi2       =     0.0000
Log likelihood = -357.36245                     Pseudo R2         =     0.1755

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.126411   .2143416    -5.26   0.000    -1.546513   -.7063092
1.worseregional |  -1.523956   .1999612    -7.62   0.000    -1.915873   -1.132039
      1.partyID |   .4432908   .2366245     1.87   0.061    -.0204847    .9070663
     1.opposeID |  -.1558345   .2216642    -0.70   0.482    -.5902885    .2786194
          _cons |   1.148404   .1963141     5.85   0.000     .7636356    1.533173
---------------------------------------------------------------------------------

. 
. *** Catalonia subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==8  & betterregional=
> =0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -229.43143  
Iteration 2:   log likelihood = -226.03983  
Iteration 3:   log likelihood = -225.93257  
Iteration 4:   log likelihood = -225.93225  
Iteration 5:   log likelihood = -225.93225  

Logistic regression                             Number of obs     =        672
                                                LR chi2(4)        =     251.61
                                                Prob > chi2       =     0.0000
Log likelihood = -225.93225                     Pseudo R2         =     0.3577

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.158937   .5508691     2.10   0.035     .0792535    2.238621
1.worseregional |  -.6031954   .3549777    -1.70   0.089    -1.298939    .0925482
      1.partyID |   4.169766   .4261672     9.78   0.000     3.334493    5.005038
     1.opposeID |  -.9709765   .3146236    -3.09   0.002    -1.587627   -.3543255
          _cons |  -2.296112   .5373996    -4.27   0.000    -3.349396   -1.242829
---------------------------------------------------------------------------------

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==11  & betterregional
> ==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -222.83252  
Iteration 2:   log likelihood = -216.40796  
Iteration 3:   log likelihood = -215.49259  
Iteration 4:   log likelihood = -215.48198  
Iteration 5:   log likelihood = -215.48196  

Logistic regression                             Number of obs     =        528
                                                LR chi2(4)        =     135.01
                                                Prob > chi2       =     0.0000
Log likelihood = -215.48196                     Pseudo R2         =     0.2385

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1971754   .2848016    -0.69   0.489    -.7553763    .3610255
1.worseregional |  -.2456889   .3142331    -0.78   0.434    -.8615744    .3701967
      1.partyID |   2.584318   .3930967     6.57   0.000     1.813862    3.354773
     1.opposeID |  -2.794268   .5978428    -4.67   0.000    -3.966018   -1.622517
          _cons |  -.9986917   .1625764    -6.14   0.000    -1.317336   -.6800478
---------------------------------------------------------------------------------

. 
. *** Bavaria subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==13  & betterregional
> ==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood = -1286.9875  
Iteration 2:   log likelihood = -1275.3557  
Iteration 3:   log likelihood = -1275.1304  
Iteration 4:   log likelihood = -1275.1301  
Iteration 5:   log likelihood = -1275.1301  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(4)        =     757.65
                                                Prob > chi2       =     0.0000
Log likelihood = -1275.1301                     Pseudo R2         =     0.2290

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.767665   .1290711    -5.95   0.000     -1.02064   -.5146903
1.worseregional |   .0449775   .1544006     0.29   0.771    -.2576421     .347597
      1.partyID |   2.330339   .1351704    17.24   0.000      2.06541    2.595268
     1.opposeID |  -1.830872   .1838194    -9.96   0.000    -2.191151   -1.470592
          _cons |  -.6901443   .0629525   -10.96   0.000    -.8135289   -.5667598
---------------------------------------------------------------------------------

. 
. 
. ********************************************************
. *** auxilliary analyses with marginal effects based on analyses in Table 7.5
. 
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==5  & betternational=
> =0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -199.33711  
Iteration 2:   log likelihood = -191.09449  
Iteration 3:   log likelihood = -190.42384  
Iteration 4:   log likelihood = -190.42331  
Iteration 5:   log likelihood = -190.42331  

Logistic regression                             Number of obs     =        537
                                                LR chi2(4)        =     255.30
                                                Prob > chi2       =     0.0000
Log likelihood = -190.42331                     Pseudo R2         =     0.4013

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.8792855   .3063073    -2.87   0.004    -1.479637   -.2789342
1.worseregional |   .3330972   .2751954     1.21   0.226    -.2062759    .8724702
      1.partyID |   2.570303   .3384496     7.59   0.000     1.906954    3.233652
     1.opposeID |  -2.749814   .4425126    -6.21   0.000    -3.617123   -1.882505
          _cons |  -.3084349   .2619905    -1.18   0.239    -.8219269    .2050571
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1064893   .0389029    -2.74   0.006    -.1827376    -.030241
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0374829   .0310365     1.21   0.227    -.0233474    .0983132
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==6  & betternational=
> =0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -240.94392  
Iteration 2:   log likelihood = -238.98126  
Iteration 3:   log likelihood = -238.95054  
Iteration 4:   log likelihood = -238.95053  

Logistic regression                             Number of obs     =        538
                                                LR chi2(4)        =     183.74
                                                Prob > chi2       =     0.0000
Log likelihood = -238.95053                     Pseudo R2         =     0.2777

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.7439554   .2822873    -2.64   0.008    -1.297228   -.1906824
1.worseregional |   .2055319   .2452893     0.84   0.402    -.2752263    .6862902
      1.partyID |   2.538938   .3289206     7.72   0.000     1.894266    3.183611
     1.opposeID |  -1.402921   .2762499    -5.08   0.000    -1.944361   -.8614813
          _cons |  -.3757716   .2435507    -1.54   0.123    -.8531223    .1015791
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1140078   .0462413    -2.47   0.014    -.2046391   -.0233766
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0287969   .0342387     0.84   0.400    -.0383096    .0959035
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==7  & betternational=
> =0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -254.85936  
Iteration 2:   log likelihood = -252.05088  
Iteration 3:   log likelihood = -250.85609  
Iteration 4:   log likelihood = -250.85436  
Iteration 5:   log likelihood = -250.85436  

Logistic regression                             Number of obs     =        661
                                                LR chi2(4)        =     150.85
                                                Prob > chi2       =     0.0000
Log likelihood = -250.85436                     Pseudo R2         =     0.2312

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6884096   .3228407    -2.13   0.033    -1.321166   -.0556534
1.worseregional |   .0799382   .2670973     0.30   0.765     -.443563    .6034393
      1.partyID |   3.607272   .4022979     8.97   0.000     2.818782    4.395761
     1.opposeID |  -1.422201   .6027299    -2.36   0.018     -2.60353    -.240872
          _cons |  -1.251115   .2928453    -4.27   0.000    -1.825081   -.6771489
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0896347   .0478554    -1.87   0.061    -.1834295      .00416
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0088938   .0294546     0.30   0.763    -.0488362    .0666238
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==9  & betternational=
> =0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -253.09884  
Iteration 2:   log likelihood = -245.73988  
Iteration 3:   log likelihood = -244.85522  
Iteration 4:   log likelihood = -244.85005  
Iteration 5:   log likelihood = -244.85005  

Logistic regression                             Number of obs     =        698
                                                LR chi2(4)        =     228.89
                                                Prob > chi2       =     0.0000
Log likelihood = -244.85005                     Pseudo R2         =     0.3185

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.310009   .3001803    -4.36   0.000    -1.898352   -.7216665
1.worseregional |   .8293593   .2563195     3.24   0.001     .3269824    1.331736
      1.partyID |   3.228042   .3710715     8.70   0.000     2.500755    3.955328
     1.opposeID |  -1.855374   .4747925    -3.91   0.000     -2.78595   -.9247976
          _cons |  -.9764774   .2965669    -3.29   0.001    -1.557738    -.395217
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1760219   .0469611    -3.75   0.000    -.2680641   -.0839798
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |    .086166   .0257777     3.34   0.001     .0356427    .1366893
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==10  & betternational
> ==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -199.97769  
Iteration 2:   log likelihood = -196.53964  
Iteration 3:   log likelihood = -196.31665  
Iteration 4:   log likelihood = -196.31608  
Iteration 5:   log likelihood = -196.31608  

Logistic regression                             Number of obs     =        478
                                                LR chi2(4)        =     113.21
                                                Prob > chi2       =     0.0000
Log likelihood = -196.31608                     Pseudo R2         =     0.2238

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.5145067   .3253954    -1.58   0.114     -1.15227    .1232566
1.worseregional |   -.268418   .3551333    -0.76   0.450    -.9644664    .4276304
      1.partyID |   2.839093   .4735886     5.99   0.000     1.910876     3.76731
     1.opposeID |   -1.80755    .373177    -4.84   0.000    -2.538963   -1.076136
          _cons |  -.8563224    .172101    -4.98   0.000    -1.193634   -.5190106
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0649753   .0400217    -1.62   0.104    -.1434163    .0134657
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0337176   .0433979    -0.78   0.437    -.1187759    .0513407
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==12  & betternational
> ==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1134.0937  
Iteration 2:   log likelihood =  -1133.965  
Iteration 3:   log likelihood = -1133.9649  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(4)        =     747.63
                                                Prob > chi2       =     0.0000
Log likelihood = -1133.9649                     Pseudo R2         =     0.2479

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6652016   .1190736    -5.59   0.000    -.8985816   -.4318216
1.worseregional |   .4691207   .1279522     3.67   0.000     .2183391    .7199024
      1.partyID |   3.207298   .2296673    13.96   0.000     2.757158    3.657437
     1.opposeID |  -1.247995    .124004   -10.06   0.000    -1.491039   -1.004952
          _cons |  -.5102669   .0742531    -6.87   0.000    -.6558002   -.3647336
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1050832   .0178665    -5.88   0.000     -.140101   -.0700655
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0803423   .0227345     3.53   0.000     .0357836    .1249011
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. **** Subnational Vote
. 
. *** Marseille
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==23  & betterregional
> ==0

Iteration 0:   log likelihood = -214.86706  
Iteration 1:   log likelihood = -161.39623  
Iteration 2:   log likelihood = -158.95963  
Iteration 3:   log likelihood = -158.88809  
Iteration 4:   log likelihood = -158.88801  
Iteration 5:   log likelihood = -158.88801  

Logistic regression                             Number of obs     =        353
                                                LR chi2(4)        =     111.96
                                                Prob > chi2       =     0.0000
Log likelihood = -158.88801                     Pseudo R2         =     0.2605

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.925759   .4654559     4.14   0.000     1.013482    2.838036
1.worseregional |  -.9573545   .2877867    -3.33   0.001    -1.521406   -.3933029
      1.partyID |    2.17359   .4194022     5.18   0.000     1.351576    2.995603
     1.opposeID |  -.9226439   .3354537    -2.75   0.006    -1.580121   -.2651668
          _cons |  -2.069754   .4427596    -4.67   0.000    -2.937546   -1.201961
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.1422753   .0420244    -3.39   0.001    -.2246416   -.0599089
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   .2492813    .047171     5.28   0.000     .1568279    .3417347
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Paris
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==22  & betterregional
> ==0

Iteration 0:   log likelihood = -433.40336  
Iteration 1:   log likelihood = -357.54954  
Iteration 2:   log likelihood = -357.36252  
Iteration 3:   log likelihood = -357.36245  
Iteration 4:   log likelihood = -357.36245  

Logistic regression                             Number of obs     =        629
                                                LR chi2(4)        =     152.08
                                                Prob > chi2       =     0.0000
Log likelihood = -357.36245                     Pseudo R2         =     0.1755

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.126411   .2143416    -5.26   0.000    -1.546513   -.7063092
1.worseregional |  -1.523956   .1999612    -7.62   0.000    -1.915873   -1.132039
      1.partyID |   .4432908   .2366245     1.87   0.061    -.0204847    .9070663
     1.opposeID |  -.1558345   .2216642    -0.70   0.482    -.5902885    .2786194
          _cons |   1.148404   .1963141     5.85   0.000     .7636356    1.533173
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3298233   .0414627    -7.95   0.000    -.4110887    -.248558
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.2386522   .0463833    -5.15   0.000    -.3295617   -.1477426
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==8  & betterregional=
> =0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -229.43143  
Iteration 2:   log likelihood = -226.03983  
Iteration 3:   log likelihood = -225.93257  
Iteration 4:   log likelihood = -225.93225  
Iteration 5:   log likelihood = -225.93225  

Logistic regression                             Number of obs     =        672
                                                LR chi2(4)        =     251.61
                                                Prob > chi2       =     0.0000
Log likelihood = -225.93225                     Pseudo R2         =     0.3577

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.158937   .5508691     2.10   0.035     .0792535    2.238621
1.worseregional |  -.6031954   .3549777    -1.70   0.089    -1.298939    .0925482
      1.partyID |   4.169766   .4261672     9.78   0.000     3.334493    5.005038
     1.opposeID |  -.9709765   .3146236    -3.09   0.002    -1.587627   -.3543255
          _cons |  -2.296112   .5373996    -4.27   0.000    -3.349396   -1.242829
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0668573   .0446057    -1.50   0.134    -.1542829    .0205683
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   .0873064   .0329436     2.65   0.008     .0227382    .1518746
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==11  & betterregional
> ==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -222.83252  
Iteration 2:   log likelihood = -216.40796  
Iteration 3:   log likelihood = -215.49259  
Iteration 4:   log likelihood = -215.48198  
Iteration 5:   log likelihood = -215.48196  

Logistic regression                             Number of obs     =        528
                                                LR chi2(4)        =     135.01
                                                Prob > chi2       =     0.0000
Log likelihood = -215.48196                     Pseudo R2         =     0.2385

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1971754   .2848016    -0.69   0.489    -.7553763    .3610255
1.worseregional |  -.2456889   .3142331    -0.78   0.434    -.8615744    .3701967
      1.partyID |   2.584318   .3930967     6.57   0.000     1.813862    3.354773
     1.opposeID |  -2.794268   .5978428    -4.67   0.000    -3.966018   -1.622517
          _cons |  -.9986917   .1625764    -6.14   0.000    -1.317336   -.6800478
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0315445   .0395249    -0.80   0.425    -.1090119    .0459229
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0256278   .0367395    -0.70   0.485    -.0976358    .0463803
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==13  & betterregional
> ==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood = -1286.9875  
Iteration 2:   log likelihood = -1275.3557  
Iteration 3:   log likelihood = -1275.1304  
Iteration 4:   log likelihood = -1275.1301  
Iteration 5:   log likelihood = -1275.1301  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(4)        =     757.65
                                                Prob > chi2       =     0.0000
Log likelihood = -1275.1301                     Pseudo R2         =     0.2290

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.767665   .1290711    -5.95   0.000     -1.02064   -.5146903
1.worseregional |   .0449775   .1544006     0.29   0.771    -.2576421     .347597
      1.partyID |   2.330339   .1351704    17.24   0.000      2.06541    2.595268
     1.opposeID |  -1.830872   .1838194    -9.96   0.000    -2.191151   -1.470592
          _cons |  -.6901443   .0629525   -10.96   0.000    -.8135289   -.5667598
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0072924   .0251159     0.29   0.772     -.041934    .0565187
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1201278   .0191438    -6.28   0.000    -.1576489   -.0826067
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. 
. *** National economic perceptions in EU election 
. 
. *** IDF EU
. 
. tab nationaleconomy if ELECID==17

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        675       69.23       69.23
          1 |        264       27.08       96.31
          2 |         36        3.69      100.00
------------+-----------------------------------
      Total |        975      100.00

. 
. *** Provence EU
. 
. tab nationaleconomy if ELECID==16

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        719       69.20       69.20
          1 |        297       28.59       97.79
          2 |         23        2.21      100.00
------------+-----------------------------------
      Total |      1,039      100.00

. 
. *** Catalonia EU
. 
. tab nationaleconomy if ELECID==20

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        581       58.98       58.98
          1 |        323       32.79       91.78
          2 |         81        8.22      100.00
------------+-----------------------------------
      Total |        985      100.00

. 
. *** Madrid EU
. 
. tab nationaleconomy if ELECID==21

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        439       45.07       45.07
          1 |        331       33.98       79.06
          2 |        204       20.94      100.00
------------+-----------------------------------
      Total |        974      100.00

. 
. *** Lower Saxony EU
. 
. tab nationaleconomy if ELECID==18

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        250       25.56       25.56
          1 |        502       51.33       76.89
          2 |        226       23.11      100.00
------------+-----------------------------------
      Total |        978      100.00

. 
. *** Bavaria EU
. 
. tab nationaleconomy if ELECID==19

nationaleco |
       nomy |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        440       15.42       15.42
          1 |      1,512       52.98       68.40
          2 |        902       31.60      100.00
------------+-----------------------------------
      Total |      2,854      100.00

. 
. *** European VOTE 
. 
. 
. *** Table 7.6
. 
. *** IDF EU
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==17 & betternational<2

Iteration 0:   log likelihood = -256.34663  
Iteration 1:   log likelihood = -177.23651  
Iteration 2:   log likelihood = -175.02871  
Iteration 3:   log likelihood = -151.10898  
Iteration 4:   log likelihood = -150.52442  
Iteration 5:   log likelihood = -150.52107  
Iteration 6:   log likelihood = -150.52107  

Logistic regression                             Number of obs     =        617
                                                LR chi2(3)        =     211.65
                                                Prob > chi2       =     0.0000
Log likelihood = -150.52107                     Pseudo R2         =     0.4128

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.605431   .3042899    -5.28   0.000    -2.201828   -1.009034
      1.partyID |   2.739462   .3320651     8.25   0.000     2.088626    3.390297
     1.opposeID |  -2.167828   .6146209    -3.53   0.000    -3.372463   -.9631935
          _cons |  -1.156492    .230113    -5.03   0.000    -1.607505   -.7054789
---------------------------------------------------------------------------------

. 
. *** Provence EU
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==16 & betternational<2

Iteration 0:   log likelihood = -218.99519  
Iteration 1:   log likelihood = -151.17981  
Iteration 2:   log likelihood = -117.57496  
Iteration 3:   log likelihood = -113.71264  
Iteration 4:   log likelihood = -113.63512  
Iteration 5:   log likelihood = -113.63509  
Iteration 6:   log likelihood = -113.63509  

Logistic regression                             Number of obs     =        591
                                                LR chi2(3)        =     210.72
                                                Prob > chi2       =     0.0000
Log likelihood = -113.63509                     Pseudo R2         =     0.4811

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -1.31143   .3769413    -3.48   0.001    -2.050222   -.5726388
      1.partyID |   3.413869   .4114572     8.30   0.000     2.607428    4.220311
     1.opposeID |  -.8613114   .5122244    -1.68   0.093    -1.865253      .14263
          _cons |  -2.079675   .3529755    -5.89   0.000    -2.771494   -1.387856
---------------------------------------------------------------------------------

. 
. *** Catalonia EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==20

Iteration 0:   log likelihood = -110.20461  
Iteration 1:   log likelihood = -77.616178  
Iteration 2:   log likelihood = -76.260839  
Iteration 3:   log likelihood = -63.708509  
Iteration 4:   log likelihood = -62.666261  
Iteration 5:   log likelihood = -62.603619  
Iteration 6:   log likelihood = -62.603435  
Iteration 7:   log likelihood = -62.603435  

Logistic regression                             Number of obs     =        602
                                                LR chi2(4)        =      95.20
                                                Prob > chi2       =     0.0000
Log likelihood = -62.603435                     Pseudo R2         =     0.4319

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.7950877   .6880654    -1.16   0.248    -2.143671    .5534958
1.betternational |    2.71735   .5941647     4.57   0.000     1.552809    3.881892
       1.partyID |   2.858798   1.018692     2.81   0.005     .8621975    4.855398
      1.opposeID |  -2.874351   1.049621    -2.74   0.006     -4.93157   -.8171316
           _cons |  -3.179373   .4700338    -6.76   0.000    -4.100623   -2.258124
----------------------------------------------------------------------------------

. 
. *** Madrid EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==21

Iteration 0:   log likelihood = -267.99303  
Iteration 1:   log likelihood = -159.99044  
Iteration 2:   log likelihood = -142.48869  
Iteration 3:   log likelihood = -138.61109  
Iteration 4:   log likelihood = -138.45903  
Iteration 5:   log likelihood = -138.45773  
Iteration 6:   log likelihood = -138.45773  

Logistic regression                             Number of obs     =        579
                                                LR chi2(4)        =     259.07
                                                Prob > chi2       =     0.0000
Log likelihood = -138.45773                     Pseudo R2         =     0.4834

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -1.706149   .5772196    -2.96   0.003    -2.837478   -.5748192
1.betternational |    2.14245   .3369728     6.36   0.000     1.481995    2.802904
       1.partyID |   2.256065   .3871341     5.83   0.000     1.497296    3.014833
      1.opposeID |  -2.684457   1.034835    -2.59   0.009    -4.712698   -.6562173
           _cons |  -2.258887   .2855478    -7.91   0.000     -2.81855   -1.699223
----------------------------------------------------------------------------------

. 
. *** Lower Saxony EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==18

Iteration 0:   log likelihood = -382.16219  
Iteration 1:   log likelihood =  -306.4691  
Iteration 2:   log likelihood = -306.23222  
Iteration 3:   log likelihood = -306.23177  
Iteration 4:   log likelihood = -306.23177  

Logistic regression                             Number of obs     =        555
                                                LR chi2(4)        =     151.86
                                                Prob > chi2       =     0.0000
Log likelihood = -306.23177                     Pseudo R2         =     0.1987

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.1764947   .2355352    -0.75   0.454    -.6381352    .2851458
1.betternational |   .6135956   .2530923     2.42   0.015     .1175439    1.109647
       1.partyID |   1.561294   .2563024     6.09   0.000      1.05895    2.063637
      1.opposeID |   -2.18133   .3432965    -6.35   0.000    -2.854178   -1.508481
           _cons |   .0408335   .1510422     0.27   0.787    -.2552037    .3368707
----------------------------------------------------------------------------------

. 
. *** Bavaria EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==19

Iteration 0:   log likelihood = -1169.3952  
Iteration 1:   log likelihood = -937.92848  
Iteration 2:   log likelihood = -935.98167  
Iteration 3:   log likelihood = -935.97458  
Iteration 4:   log likelihood = -935.97458  

Logistic regression                             Number of obs     =      1,701
                                                LR chi2(4)        =     466.84
                                                Prob > chi2       =     0.0000
Log likelihood = -935.97458                     Pseudo R2         =     0.1996

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.2484298   .1653382    -1.50   0.133    -.5724868    .0756272
1.betternational |   .5707217   .1271479     4.49   0.000     .3215164    .8199271
       1.partyID |   1.583614   .1262073    12.55   0.000     1.336252    1.830976
      1.opposeID |   -1.85663   .2040309    -9.10   0.000    -2.256523   -1.456737
           _cons |  -.2479213   .0886456    -2.80   0.005    -.4216634   -.0741791
----------------------------------------------------------------------------------

. 
. 
. 
. ********************************************************
. *** Additional EU election models
. *** auxilliary analyses with marginal effects based on analyses in Table 7.6
. 
. 
. **** National Vote
. 
. *** IDF national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==5  & betternational=
> =0

Iteration 0:   log likelihood = -318.07533  
Iteration 1:   log likelihood = -199.33711  
Iteration 2:   log likelihood = -191.09449  
Iteration 3:   log likelihood = -190.42384  
Iteration 4:   log likelihood = -190.42331  
Iteration 5:   log likelihood = -190.42331  

Logistic regression                             Number of obs     =        537
                                                LR chi2(4)        =     255.30
                                                Prob > chi2       =     0.0000
Log likelihood = -190.42331                     Pseudo R2         =     0.4013

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.8792855   .3063073    -2.87   0.004    -1.479637   -.2789342
1.worseregional |   .3330972   .2751954     1.21   0.226    -.2062759    .8724702
      1.partyID |   2.570303   .3384496     7.59   0.000     1.906954    3.233652
     1.opposeID |  -2.749814   .4425126    -6.21   0.000    -3.617123   -1.882505
          _cons |  -.3084349   .2619905    -1.18   0.239    -.8219269    .2050571
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1064893   .0389029    -2.74   0.006    -.1827376    -.030241
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        537
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0374829   .0310365     1.21   0.227    -.0233474    .0983132
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==6  & betternational=
> =0

Iteration 0:   log likelihood = -330.81814  
Iteration 1:   log likelihood = -240.94392  
Iteration 2:   log likelihood = -238.98126  
Iteration 3:   log likelihood = -238.95054  
Iteration 4:   log likelihood = -238.95053  

Logistic regression                             Number of obs     =        538
                                                LR chi2(4)        =     183.74
                                                Prob > chi2       =     0.0000
Log likelihood = -238.95053                     Pseudo R2         =     0.2777

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.7439554   .2822873    -2.64   0.008    -1.297228   -.1906824
1.worseregional |   .2055319   .2452893     0.84   0.402    -.2752263    .6862902
      1.partyID |   2.538938   .3289206     7.72   0.000     1.894266    3.183611
     1.opposeID |  -1.402921   .2762499    -5.08   0.000    -1.944361   -.8614813
          _cons |  -.3757716   .2435507    -1.54   0.123    -.8531223    .1015791
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1140078   .0462413    -2.47   0.014    -.2046391   -.0233766
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        538
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0287969   .0342387     0.84   0.400    -.0383096    .0959035
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==7  & betternational=
> =0

Iteration 0:   log likelihood = -326.28115  
Iteration 1:   log likelihood = -254.85936  
Iteration 2:   log likelihood = -252.05088  
Iteration 3:   log likelihood = -250.85609  
Iteration 4:   log likelihood = -250.85436  
Iteration 5:   log likelihood = -250.85436  

Logistic regression                             Number of obs     =        661
                                                LR chi2(4)        =     150.85
                                                Prob > chi2       =     0.0000
Log likelihood = -250.85436                     Pseudo R2         =     0.2312

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6884096   .3228407    -2.13   0.033    -1.321166   -.0556534
1.worseregional |   .0799382   .2670973     0.30   0.765     -.443563    .6034393
      1.partyID |   3.607272   .4022979     8.97   0.000     2.818782    4.395761
     1.opposeID |  -1.422201   .6027299    -2.36   0.018     -2.60353    -.240872
          _cons |  -1.251115   .2928453    -4.27   0.000    -1.825081   -.6771489
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0896347   .0478554    -1.87   0.061    -.1834295      .00416
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        661
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0088938   .0294546     0.30   0.763    -.0488362    .0666238
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==9  & betternational=
> =0

Iteration 0:   log likelihood = -359.29746  
Iteration 1:   log likelihood = -253.09884  
Iteration 2:   log likelihood = -245.73988  
Iteration 3:   log likelihood = -244.85522  
Iteration 4:   log likelihood = -244.85005  
Iteration 5:   log likelihood = -244.85005  

Logistic regression                             Number of obs     =        698
                                                LR chi2(4)        =     228.89
                                                Prob > chi2       =     0.0000
Log likelihood = -244.85005                     Pseudo R2         =     0.3185

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.310009   .3001803    -4.36   0.000    -1.898352   -.7216665
1.worseregional |   .8293593   .2563195     3.24   0.001     .3269824    1.331736
      1.partyID |   3.228042   .3710715     8.70   0.000     2.500755    3.955328
     1.opposeID |  -1.855374   .4747925    -3.91   0.000     -2.78595   -.9247976
          _cons |  -.9764774   .2965669    -3.29   0.001    -1.557738    -.395217
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1760219   .0469611    -3.75   0.000    -.2680641   -.0839798
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        698
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |    .086166   .0257777     3.34   0.001     .0356427    .1366893
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==10  & betternational
> ==0

Iteration 0:   log likelihood = -252.92087  
Iteration 1:   log likelihood = -199.97769  
Iteration 2:   log likelihood = -196.53964  
Iteration 3:   log likelihood = -196.31665  
Iteration 4:   log likelihood = -196.31608  
Iteration 5:   log likelihood = -196.31608  

Logistic regression                             Number of obs     =        478
                                                LR chi2(4)        =     113.21
                                                Prob > chi2       =     0.0000
Log likelihood = -196.31608                     Pseudo R2         =     0.2238

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.5145067   .3253954    -1.58   0.114     -1.15227    .1232566
1.worseregional |   -.268418   .3551333    -0.76   0.450    -.9644664    .4276304
      1.partyID |   2.839093   .4735886     5.99   0.000     1.910876     3.76731
     1.opposeID |   -1.80755    .373177    -4.84   0.000    -2.538963   -1.076136
          _cons |  -.8563224    .172101    -4.98   0.000    -1.193634   -.5190106
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0649753   .0400217    -1.62   0.104    -.1434163    .0134657
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        478
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0337176   .0433979    -0.78   0.437    -.1187759    .0513407
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria national
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==12  & betternational
> ==0

Iteration 0:   log likelihood = -1507.7795  
Iteration 1:   log likelihood = -1134.0937  
Iteration 2:   log likelihood =  -1133.965  
Iteration 3:   log likelihood = -1133.9649  

Logistic regression                             Number of obs     =      2,290
                                                LR chi2(4)        =     747.63
                                                Prob > chi2       =     0.0000
Log likelihood = -1133.9649                     Pseudo R2         =     0.2479

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.6652016   .1190736    -5.59   0.000    -.8985816   -.4318216
1.worseregional |   .4691207   .1279522     3.67   0.000     .2183391    .7199024
      1.partyID |   3.207298   .2296673    13.96   0.000     2.757158    3.657437
     1.opposeID |  -1.247995    .124004   -10.06   0.000    -1.491039   -1.004952
          _cons |  -.5102669   .0742531    -6.87   0.000    -.6558002   -.3647336
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1050832   .0178665    -5.88   0.000     -.140101   -.0700655
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =      2,290
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0803423   .0227345     3.53   0.000     .0357836    .1249011
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. **** Subnational Vote
. 
. *** Marseille
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==23  & betterregional
> ==0

Iteration 0:   log likelihood = -214.86706  
Iteration 1:   log likelihood = -161.39623  
Iteration 2:   log likelihood = -158.95963  
Iteration 3:   log likelihood = -158.88809  
Iteration 4:   log likelihood = -158.88801  
Iteration 5:   log likelihood = -158.88801  

Logistic regression                             Number of obs     =        353
                                                LR chi2(4)        =     111.96
                                                Prob > chi2       =     0.0000
Log likelihood = -158.88801                     Pseudo R2         =     0.2605

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.925759   .4654559     4.14   0.000     1.013482    2.838036
1.worseregional |  -.9573545   .2877867    -3.33   0.001    -1.521406   -.3933029
      1.partyID |    2.17359   .4194022     5.18   0.000     1.351576    2.995603
     1.opposeID |  -.9226439   .3354537    -2.75   0.006    -1.580121   -.2651668
          _cons |  -2.069754   .4427596    -4.67   0.000    -2.937546   -1.201961
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.1422753   .0420244    -3.39   0.001    -.2246416   -.0599089
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        353
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   .2492813    .047171     5.28   0.000     .1568279    .3417347
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Paris
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==22  & betterregional
> ==0

Iteration 0:   log likelihood = -433.40336  
Iteration 1:   log likelihood = -357.54954  
Iteration 2:   log likelihood = -357.36252  
Iteration 3:   log likelihood = -357.36245  
Iteration 4:   log likelihood = -357.36245  

Logistic regression                             Number of obs     =        629
                                                LR chi2(4)        =     152.08
                                                Prob > chi2       =     0.0000
Log likelihood = -357.36245                     Pseudo R2         =     0.1755

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.126411   .2143416    -5.26   0.000    -1.546513   -.7063092
1.worseregional |  -1.523956   .1999612    -7.62   0.000    -1.915873   -1.132039
      1.partyID |   .4432908   .2366245     1.87   0.061    -.0204847    .9070663
     1.opposeID |  -.1558345   .2216642    -0.70   0.482    -.5902885    .2786194
          _cons |   1.148404   .1963141     5.85   0.000     .7636356    1.533173
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.3298233   .0414627    -7.95   0.000    -.4110887    -.248558
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        629
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.2386522   .0463833    -5.15   0.000    -.3295617   -.1477426
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==8  & betterregional=
> =0

Iteration 0:   log likelihood =  -351.7386  
Iteration 1:   log likelihood = -229.43143  
Iteration 2:   log likelihood = -226.03983  
Iteration 3:   log likelihood = -225.93257  
Iteration 4:   log likelihood = -225.93225  
Iteration 5:   log likelihood = -225.93225  

Logistic regression                             Number of obs     =        672
                                                LR chi2(4)        =     251.61
                                                Prob > chi2       =     0.0000
Log likelihood = -225.93225                     Pseudo R2         =     0.3577

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   1.158937   .5508691     2.10   0.035     .0792535    2.238621
1.worseregional |  -.6031954   .3549777    -1.70   0.089    -1.298939    .0925482
      1.partyID |   4.169766   .4261672     9.78   0.000     3.334493    5.005038
     1.opposeID |  -.9709765   .3146236    -3.09   0.002    -1.587627   -.3543255
          _cons |  -2.296112   .5373996    -4.27   0.000    -3.349396   -1.242829
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0668573   .0446057    -1.50   0.134    -.1542829    .0205683
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        672
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   .0873064   .0329436     2.65   0.008     .0227382    .1518746
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid subnational Missing
. 
. *** Lower Saxony subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==11  & betterregional
> ==0

Iteration 0:   log likelihood = -282.98682  
Iteration 1:   log likelihood = -222.83252  
Iteration 2:   log likelihood = -216.40796  
Iteration 3:   log likelihood = -215.49259  
Iteration 4:   log likelihood = -215.48198  
Iteration 5:   log likelihood = -215.48196  

Logistic regression                             Number of obs     =        528
                                                LR chi2(4)        =     135.01
                                                Prob > chi2       =     0.0000
Log likelihood = -215.48196                     Pseudo R2         =     0.2385

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1971754   .2848016    -0.69   0.489    -.7553763    .3610255
1.worseregional |  -.2456889   .3142331    -0.78   0.434    -.8615744    .3701967
      1.partyID |   2.584318   .3930967     6.57   0.000     1.813862    3.354773
     1.opposeID |  -2.794268   .5978428    -4.67   0.000    -3.966018   -1.622517
          _cons |  -.9986917   .1625764    -6.14   0.000    -1.317336   -.6800478
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |  -.0315445   .0395249    -0.80   0.425    -.1090119    .0459229
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        528
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0256278   .0367395    -0.70   0.485    -.0976358    .0463803
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria subnational
. logit INCUMBENT i.worsenational i.worseregional i.partyID i.opposeID if ELECID==13  & betterregional
> ==0

Iteration 0:   log likelihood =  -1653.957  
Iteration 1:   log likelihood = -1286.9875  
Iteration 2:   log likelihood = -1275.3557  
Iteration 3:   log likelihood = -1275.1304  
Iteration 4:   log likelihood = -1275.1301  
Iteration 5:   log likelihood = -1275.1301  

Logistic regression                             Number of obs     =      2,587
                                                LR chi2(4)        =     757.65
                                                Prob > chi2       =     0.0000
Log likelihood = -1275.1301                     Pseudo R2         =     0.2290

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.767665   .1290711    -5.95   0.000     -1.02064   -.5146903
1.worseregional |   .0449775   .1544006     0.29   0.771    -.2576421     .347597
      1.partyID |   2.330339   .1351704    17.24   0.000      2.06541    2.595268
     1.opposeID |  -1.830872   .1838194    -9.96   0.000    -2.191151   -1.470592
          _cons |  -.6901443   .0629525   -10.96   0.000    -.8135289   -.5667598
---------------------------------------------------------------------------------

. margins, dydx(worseregional)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worseregional

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worseregional |   .0072924   .0251159     0.29   0.772     -.041934    .0565187
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      2,587
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1201278   .0191438    -6.28   0.000    -.1576489   -.0826067
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Table 7.6 
. 
. *** IDF EU
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==17 & betternational<2

Iteration 0:   log likelihood = -256.34663  
Iteration 1:   log likelihood = -177.23651  
Iteration 2:   log likelihood = -175.02871  
Iteration 3:   log likelihood = -151.10898  
Iteration 4:   log likelihood = -150.52442  
Iteration 5:   log likelihood = -150.52107  
Iteration 6:   log likelihood = -150.52107  

Logistic regression                             Number of obs     =        617
                                                LR chi2(3)        =     211.65
                                                Prob > chi2       =     0.0000
Log likelihood = -150.52107                     Pseudo R2         =     0.4128

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -1.605431   .3042899    -5.28   0.000    -2.201828   -1.009034
      1.partyID |   2.739462   .3320651     8.25   0.000     2.088626    3.390297
     1.opposeID |  -2.167828   .6146209    -3.53   0.000    -3.372463   -.9631935
          _cons |  -1.156492    .230113    -5.03   0.000    -1.607505   -.7054789
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        617
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1375535   .0288747    -4.76   0.000    -.1941469   -.0809602
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Provence EU
. logit INCUMBENT i.worsenational i.partyID i.opposeID if ELECID==16 & betternational<2

Iteration 0:   log likelihood = -218.99519  
Iteration 1:   log likelihood = -151.17981  
Iteration 2:   log likelihood = -117.57496  
Iteration 3:   log likelihood = -113.71264  
Iteration 4:   log likelihood = -113.63512  
Iteration 5:   log likelihood = -113.63509  
Iteration 6:   log likelihood = -113.63509  

Logistic regression                             Number of obs     =        591
                                                LR chi2(3)        =     210.72
                                                Prob > chi2       =     0.0000
Log likelihood = -113.63509                     Pseudo R2         =     0.4811

---------------------------------------------------------------------------------
      INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -1.31143   .3769413    -3.48   0.001    -2.050222   -.5726388
      1.partyID |   3.413869   .4114572     8.30   0.000     2.607428    4.220311
     1.opposeID |  -.8613114   .5122244    -1.68   0.093    -1.865253      .14263
          _cons |  -2.079675   .3529755    -5.89   0.000    -2.771494   -1.387856
---------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        591
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |   -.083965   .0282067    -2.98   0.003    -.1392491   -.0286809
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Catalonia EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==20

Iteration 0:   log likelihood = -110.20461  
Iteration 1:   log likelihood = -77.616178  
Iteration 2:   log likelihood = -76.260839  
Iteration 3:   log likelihood = -63.708509  
Iteration 4:   log likelihood = -62.666261  
Iteration 5:   log likelihood = -62.603619  
Iteration 6:   log likelihood = -62.603435  
Iteration 7:   log likelihood = -62.603435  

Logistic regression                             Number of obs     =        602
                                                LR chi2(4)        =      95.20
                                                Prob > chi2       =     0.0000
Log likelihood = -62.603435                     Pseudo R2         =     0.4319

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.7950877   .6880654    -1.16   0.248    -2.143671    .5534958
1.betternational |    2.71735   .5941647     4.57   0.000     1.552809    3.881892
       1.partyID |   2.858798   1.018692     2.81   0.005     .8621975    4.855398
      1.opposeID |  -2.874351   1.049621    -2.74   0.006     -4.93157   -.8171316
           _cons |  -3.179373   .4700338    -6.76   0.000    -4.100623   -2.258124
----------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        602
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0207606   .0176445    -1.18   0.239    -.0553431    .0138219
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(betternational)

Average marginal effects                        Number of obs     =        602
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.betternational

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.betternational |    .161555   .0630399     2.56   0.010     .0379989     .285111
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Madrid EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==21

Iteration 0:   log likelihood = -267.99303  
Iteration 1:   log likelihood = -159.99044  
Iteration 2:   log likelihood = -142.48869  
Iteration 3:   log likelihood = -138.61109  
Iteration 4:   log likelihood = -138.45903  
Iteration 5:   log likelihood = -138.45773  
Iteration 6:   log likelihood = -138.45773  

Logistic regression                             Number of obs     =        579
                                                LR chi2(4)        =     259.07
                                                Prob > chi2       =     0.0000
Log likelihood = -138.45773                     Pseudo R2         =     0.4834

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -1.706149   .5772196    -2.96   0.003    -2.837478   -.5748192
1.betternational |    2.14245   .3369728     6.36   0.000     1.481995    2.802904
       1.partyID |   2.256065   .3871341     5.83   0.000     1.497296    3.014833
      1.opposeID |  -2.684457   1.034835    -2.59   0.009    -4.712698   -.6562173
           _cons |  -2.258887   .2855478    -7.91   0.000     -2.81855   -1.699223
----------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        579
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.1190005   .0360222    -3.30   0.001    -.1896027   -.0483983
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(betternational)

Average marginal effects                        Number of obs     =        579
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.betternational

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.betternational |   .2146349   .0429375     5.00   0.000     .1304789    .2987909
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Lower Saxony EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==18

Iteration 0:   log likelihood = -382.16219  
Iteration 1:   log likelihood =  -306.4691  
Iteration 2:   log likelihood = -306.23222  
Iteration 3:   log likelihood = -306.23177  
Iteration 4:   log likelihood = -306.23177  

Logistic regression                             Number of obs     =        555
                                                LR chi2(4)        =     151.86
                                                Prob > chi2       =     0.0000
Log likelihood = -306.23177                     Pseudo R2         =     0.1987

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.1764947   .2355352    -0.75   0.454    -.6381352    .2851458
1.betternational |   .6135956   .2530923     2.42   0.015     .1175439    1.109647
       1.partyID |   1.561294   .2563024     6.09   0.000      1.05895    2.063637
      1.opposeID |   -2.18133   .3432965    -6.35   0.000    -2.854178   -1.508481
           _cons |   .0408335   .1510422     0.27   0.787    -.2552037    .3368707
----------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =        555
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0332815   .0447324    -0.74   0.457    -.1209555    .0543925
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(betternational)

Average marginal effects                        Number of obs     =        555
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.betternational

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.betternational |   .1137569    .045517     2.50   0.012     .0245452    .2029686
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *** Bavaria EU
. logit INCUMBENT i.worsenational i.betternational i.partyID i.opposeID if ELECID==19

Iteration 0:   log likelihood = -1169.3952  
Iteration 1:   log likelihood = -937.92848  
Iteration 2:   log likelihood = -935.98167  
Iteration 3:   log likelihood = -935.97458  
Iteration 4:   log likelihood = -935.97458  

Logistic regression                             Number of obs     =      1,701
                                                LR chi2(4)        =     466.84
                                                Prob > chi2       =     0.0000
Log likelihood = -935.97458                     Pseudo R2         =     0.1996

----------------------------------------------------------------------------------
       INCUMBENT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 1.worsenational |  -.2484298   .1653382    -1.50   0.133    -.5724868    .0756272
1.betternational |   .5707217   .1271479     4.49   0.000     .3215164    .8199271
       1.partyID |   1.583614   .1262073    12.55   0.000     1.336252    1.830976
      1.opposeID |   -1.85663   .2040309    -9.10   0.000    -2.256523   -1.456737
           _cons |  -.2479213   .0886456    -2.80   0.005    -.4216634   -.0741791
----------------------------------------------------------------------------------

. margins, dydx(worsenational)

Average marginal effects                        Number of obs     =      1,701
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.worsenational

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
1.worsenational |  -.0465968   .0313057    -1.49   0.137    -.1079547    .0147612
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins, dydx(betternational)

Average marginal effects                        Number of obs     =      1,701
Model VCE    : OIM

Expression   : Pr(INCUMBENT), predict()
dy/dx w.r.t. : 1.betternational

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
1.betternational |   .1067206   .0235294     4.54   0.000     .0606038    .1528374
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. log close
      name:  <unnamed>
       log:  C:\Users\sgolder\Dropbox\OUP_Multilevel_Book\OUP multilevel electoral behavior book\chapt
> er 7\replication\ch7_analyses.log
  log type:  text
 closed on:  22 Jun 2017, 22:04:49
------------------------------------------------------------------------------------------------------
